p_table <- function(tab_data, ...) {
  tab_data_2 <- deparse(substitute(tab_data))
  
  table_p <- do.call(CreateTableOne, 
                     list(data = as.name(tab_data_2), includeNA = TRUE, ...))
  table_p_out <- print(table_p,
                       showAllLevels = TRUE,
                       printToggle = FALSE)
  kable(table_p_out,
        align = "c")
}
uni_var <- function(test_var, data_imp) {
                
        cat("_________________________________________________")
        cat("\n")
        cat("   \n##", test_var)
        cat("\n")
        cat("_________________________________________________")
        cat("\n")
        
        f <- as.formula(paste("Surv(DX_LASTCONTACT_DEATH_MONTHS, PUF_VITAL_STATUS == 0)",
                              as.name(test_var),
                              sep = " ~ " ))
        
        data_imp_2 <- deparse(substitute(data_imp))
        km_fit <- do.call("survfit", list(formula = f, data = as.name(data_imp_2)))
        print(km_fit)
        cat("\n")
        print(summary(km_fit, times = c(12, 24, 36, 48, 60, 120)))
        cat("\n")
        cat("\n")
        cat("\n")
        cat("   \n## Univariable Cox Proportional Hazard Model for: ", test_var)
        cat("\n")
        cat("\n")
        n_levels <- nlevels(data_imp[[test_var]])
        if(n_levels == 1){
                print("Only one level, no Cox model performed")
                cat("\n")
        } else {
                cox_fit <- do.call("coxph", list(formula = f, data = as.name(data_imp_2)))
                print(summary(cox_fit))
                cat("\n")
                
                do.call("ggforest",
                         list(model = cox_fit, data = as.name(data_imp_2)))
        }
        cat("\n")
        cat("\n")
        cat("\n")
        cat("   \n## Unadjusted Kaplan Meier Overall Survival Curve for: ", test_var)
        p <- do.call("ggsurvplot",
                     list(fit = km_fit, data = as.name(data_imp_2),
                          palette = "jco", censor = FALSE, legend = "right",
                          linetype = "strata", xlab = "Time (Months)"))
        print(p)
}
f_plot <- function(test_var, data_imp){
                
        cat("_________________________________________________")
        cat("\n")
        cat("   \n##", test_var)
        cat("\n")
        cat("_________________________________________________")
        cat("\n")
        
        f <- as.formula(paste(as.name(test_var),
                              "AGE + SEX + T_SIZE + FACILITY_TYPE_F + FACILITY_LOCATION_F + YEAR_OF_DIAGNOSIS",
                              sep = " ~ " ))
        
        data_imp_2 <- deparse(substitute(data_imp))
        
        fit_fn <- do.call("glm", 
                       list(formula = f, 
                            data = as.name(data_imp_2), 
                            family = "binomial"))
        
        print(summary(fit_fn))
        
        or <- as.data.frame(exp(coefficients(fit_fn)))
        or$Variable <- rownames(or)
        rownames(or) <- c()
        names(or) <- c('OddsRatio', 'Variable')
        ci <- as.data.frame(exp(confint(fit_fn)))
        ci$Variable <- rownames(ci)
        rownames(ci) <- c()
        p_val_list <- tidy(fit_fn) %>%
        select(term, p.value) %>%
        rename(Variable = term) %>%
        mutate(p.value = round(p.value, 4))
        p_val_list$p.value <- as.character(p_val_list$p.value)
        p_val_list$p.value[p_val_list$p.value == "0"] <- "< 0.0001"
        all <- full_join(or, ci, by = 'Variable')
        all <- full_join(all, p_val_list, by = "Variable")
        names(all) <- c('OddsRatio', 'Variable', 'Lower', 'Upper', "p_value")
        all <- na.omit(all)
        all <- all %>%
        filter(Variable != '(Intercept)') 
        text <- cbind(c("Variable", as.character(all$Variable)), 
              c("Odds Ratio", as.character(round(all$OddsRatio, 2))),
              c("Lower CI", as.character(round(all$Lower, 2))),
              c("Upper CI", as.character(round(all$Upper, 2))),
              c("p Value", all$p_value))
        forestplot(text, 
           mean = c(NA, all$OddsRatio), 
           lower = c(NA, all$Lower), 
           upper = c(NA, all$Upper), 
           new_page =   TRUE, zero = 1, 
           clip = c(0.1, 100),
           hrzl_lines = list("2" = gpar(col="#444444")),
           vertices = TRUE,
           graph.pos = 2,
           xlab = "Odds Ratio (log)",
           align = "c",
           txt_gp = fpTxtGp(cex = 0.7),
           xticks = getTicks(low = all$Lower,
                             high = all$Upper,
                             clip=c(-Inf, Inf),
                             exp=TRUE),
           boxsize = 0.1)
    
}
col.width <- c(37, 10, 1, 1, 3, 1, 2, 1, 2, 1, 1, 1, 1, 1, 1, 8, 2, 2, 2, 4, 4, 1, 4, 1, 1,
               1, 3, 2, 2, 8, 2, 5, 5, 5, 4, 5, 5, 5,4, 2, 1, 2, 1, 3, 1, 1, 1, 1, 1, 1, 3,
               3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 6, 8,
               8, 8, 2, 1, 1, 1, 1, 8, 1, 1, 8, 1, 1, 2, 2, 5, 2, 5, 3, 1, 3, 1, 8, 8, 2, 8,
               2, 8, 2, 2, 1, 8, 1, 1, 1, 1, 1, 8, 1, 2, 2, 2, 2, 2, 1, 1, 1, 2, 1, 3, 1, 1,
               1, 1, 1, 1, 1, 1, 1)
col.names.abr <- c("PUF_CASE_ID", "PUF_FACILITY_ID", "FACILITY_TYPE_CD", "FACILITY_LOCATION_CD",
                   "AGE", "SEX", "RACE", "SPANISH_HISPANIC_ORIGIN", "INSURANCE_STATUS",
                   "MED_INC_QUAR_00", "NO_HSD_QUAR_00", "UR_CD_03", "MED_INC_QUAR_12", "NO_HSD_QUAR_12",
                   "UR_CD_13", "CROWFLY", "CDCC_TOTAL_BEST", "SEQUENCE_NUMBER", "CLASS_OF_CASE",
                   "YEAR_OF_DIAGNOSIS", "PRIMARY_SITE", "LATERALITY", "HISTOLOGY", "BEHAVIOR", "GRADE",
                   "DIAGNOSTIC_CONFIRMATION", "TUMOR_SIZE", "REGIONAL_NODES_POSITIVE",
                   "REGIONAL_NODES_EXAMINED", "DX_STAGING_PROC_DAYS", "RX_SUMM_DXSTG_PROC", "TNM_CLIN_T",
                   "TNM_CLIN_N", "TNM_CLIN_M", "TNM_CLIN_STAGE_GROUP", "TNM_PATH_T", "TNM_PATH_N", "TNM_PATH_M",
                   "TNM_PATH_STAGE_GROUP", "TNM_EDITION_NUMBER", "ANALYTIC_STAGE_GROUP", "CS_METS_AT_DX",
                   "CS_METS_EVAL", "CS_EXTENSION", "CS_TUMOR_SIZEEXT_EVAL", "CS_METS_DX_BONE", "CS_METS_DX_BRAIN",
                   "CS_METS_DX_LIVER", "CS_METS_DX_LUNG", "LYMPH_VASCULAR_INVASION", "CS_SITESPECIFIC_FACTOR_1",
                   "CS_SITESPECIFIC_FACTOR_2", "CS_SITESPECIFIC_FACTOR_3", "CS_SITESPECIFIC_FACTOR_4",
                   "CS_SITESPECIFIC_FACTOR_5", "CS_SITESPECIFIC_FACTOR_6", "CS_SITESPECIFIC_FACTOR_7",
                   "CS_SITESPECIFIC_FACTOR_8", "CS_SITESPECIFIC_FACTOR_9", "CS_SITESPECIFIC_FACTOR_10",
                   "CS_SITESPECIFIC_FACTOR_11", "CS_SITESPECIFIC_FACTOR_12", "CS_SITESPECIFIC_FACTOR_13",
                   "CS_SITESPECIFIC_FACTOR_14", "CS_SITESPECIFIC_FACTOR_15", "CS_SITESPECIFIC_FACTOR_16",
                   "CS_SITESPECIFIC_FACTOR_17", "CS_SITESPECIFIC_FACTOR_18", "CS_SITESPECIFIC_FACTOR_19",
                   "CS_SITESPECIFIC_FACTOR_20", "CS_SITESPECIFIC_FACTOR_21", "CS_SITESPECIFIC_FACTOR_22",
                   "CS_SITESPECIFIC_FACTOR_23", "CS_SITESPECIFIC_FACTOR_24", "CS_SITESPECIFIC_FACTOR_25",
                   "CS_VERSION_LATEST", "DX_RX_STARTED_DAYS", "DX_SURG_STARTED_DAYS", "DX_DEFSURG_STARTED_DAYS",
                   "RX_SUMM_SURG_PRIM_SITE", "RX_HOSP_SURG_APPR_2010", "RX_SUMM_SURGICAL_MARGINS",
                   "RX_SUMM_SCOPE_REG_LN_SUR", "RX_SUMM_SURG_OTH_REGDIS", "SURG_DISCHARGE_DAYS", "READM_HOSP_30_DAYS",
                   "REASON_FOR_NO_SURGERY", "DX_RAD_STARTED_DAYS", "RX_SUMM_RADIATION", "RAD_LOCATION_OF_RX",
                   "RAD_TREAT_VOL", "RAD_REGIONAL_RX_MODALITY", "RAD_REGIONAL_DOSE_CGY", "RAD_BOOST_RX_MODALITY",
                   "RAD_BOOST_DOSE_CGY", "RAD_NUM_TREAT_VOL", "RX_SUMM_SURGRAD_SEQ", "RAD_ELAPSED_RX_DAYS",
                   "REASON_FOR_NO_RADIATION", "DX_SYSTEMIC_STARTED_DAYS", "DX_CHEMO_STARTED_DAYS", "RX_SUMM_CHEMO",
                   "DX_HORMONE_STARTED_DAYS", "RX_SUMM_HORMONE", "DX_IMMUNO_STARTED_DAYS", "RX_SUMM_IMMUNOTHERAPY",
                   "RX_SUMM_TRNSPLNT_ENDO", "RX_SUMM_SYSTEMIC_SUR_SEQ", "DX_OTHER_STARTED_DAYS", "RX_SUMM_OTHER",
                   "PALLIATIVE_CARE", "RX_SUMM_TREATMENT_STATUS", "PUF_30_DAY_MORT_CD", "PUF_90_DAY_MORT_CD",
                   "DX_LASTCONTACT_DEATH_MONTHS", "PUF_VITAL_STATUS", "RX_HOSP_SURG_PRIM_SITE", "RX_HOSP_CHEMO",
                   "RX_HOSP_IMMUNOTHERAPY", "RX_HOSP_HORMONE", "RX_HOSP_OTHER", "PUF_MULT_SOURCE", "REFERENCE_DATE_FLAG",
                   "RX_SUMM_SCOPE_REG_LN_2012", "RX_HOSP_DXSTG_PROC", "PALLIATIVE_CARE_HOSP", "TUMOR_SIZE_SUMMARY",
                   "METS_AT_DX_OTHER", "METS_AT_DX_DISTANT_LN", "METS_AT_DX_BONE", "METS_AT_DX_BRAIN",
                   "METS_AT_DX_LIVER", "METS_AT_DX_LUNG", "NO_HSD_QUAR_16", "MED_INC_QUAR_16", "MEDICAID_EXPN_CODE")
#Read in data for each subsite
lip <- read_fwf('NCDBPUF_Lip.3.2016.0.dat', 
                       fwf_widths(col.width, col_names = col.names.abr),
                       col_types = cols(.default = col_character()))
melanoma <- read_fwf('NCDBPUF_Melanoma.3.2016.0.dat', 
                       fwf_widths(col.width, col_names = col.names.abr),
                       col_types = cols(.default = col_character()))
                       
skin <- read_fwf('NCDBPUF_OtSkin.3.2016.0.dat', 
                       fwf_widths(col.width, col_names = col.names.abr),
                       col_types = cols(.default = col_character()))
hodgextr <- read_fwf('NCDBPUF_HodgExtr.3.2016.0.dat', 
                       fwf_widths(col.width, col_names = col.names.abr),
                       col_types = cols(.default = col_character()))
hodgndal <- read_fwf('NCDBPUF_HodgNdal.3.2016.0.dat', 
                       fwf_widths(col.width, col_names = col.names.abr),
                       col_types = cols(.default = col_character()))
NHLndal <- read_fwf('NCDBPUF_NHLNdal.3.2016.0.dat', 
                       fwf_widths(col.width, col_names = col.names.abr),
                       col_types = cols(.default = col_character()))
NHLextr <- read_fwf('NCDBPUF_NHLExtr.3.2016.0.dat', 
                       fwf_widths(col.width, col_names = col.names.abr),
                       col_types = cols(.default = col_character()))
breast <-  read_fwf('NCDBPUF_Breast.3.2016.0.dat', 
                       fwf_widths(col.width, col_names = col.names.abr),
                       col_types = cols(.default = col_character()))
vulva <-  read_fwf('NCDBPUF_Vulva.3.2016.0.dat', 
                       fwf_widths(col.width, col_names = col.names.abr),
                       col_types = cols(.default = col_character()))
vagina <-  read_fwf('NCDBPUF_Vagina.3.2016.0.dat', 
                       fwf_widths(col.width, col_names = col.names.abr),
                       col_types = cols(.default = col_character()))
penis <-  read_fwf('NCDBPUF_Penis.3.2016.0.dat', 
                       fwf_widths(col.width, col_names = col.names.abr),
                       col_types = cols(.default = col_character()))
otleuk <- read_fwf('NCDBPUF_OtLeuk.3.2016.0.dat', 
                       fwf_widths(col.width, col_names = col.names.abr),
                       col_types = cols(.default = col_character()))
  
otheracuteleuk  <- read_fwf('NCDBPUF_OtAcLeuk.3.2016.0.dat', 
                       fwf_widths(col.width, col_names = col.names.abr),
                       col_types = cols(.default = col_character()))
  
ALL  <- read_fwf('NCDBPUF_ALymLeuk.3.2016.0.dat', 
                       fwf_widths(col.width, col_names = col.names.abr),
                       col_types = cols(.default = col_character()))
#Combine data for all subsites
dat <- bind_rows(lip, melanoma, skin, hodgextr, hodgndal, NHLndal, breast, 
                 vulva, vagina, penis, NHLextr, otleuk, otheracuteleuk, ALL)
rm(lip, melanoma, skin, hodgextr, hodgndal, NHLndal, breast, vulva, vagina, 
   penis, NHLextr, otleuk, otheracuteleuk, ALL)
prim_site_text <- data_frame(PRIMARY_SITE = c(
#NHL sites
"C000", 
"C001", 
"C002", 
"C003", 
"C004", 
"C005", 
"C006", 
"C008",
"C009", 
"C019", 
"C020", 
"C021",
"C022", 
"C023", 
"C024", 
"C028", 
"C029",
"C030",
"C031",
"C039", 
"C040", 
"C041", 
"C048",
"C049", 
"C050", 
"C051", 
"C052", 
"C058", 
"C059",
"C060", 
"C061", 
"C062", 
"C068", 
"C069", 
"C079",  
"C098",
"C099",
"C111",
"C142",
"C300",
"C379",
"C420",
"C422",
"C424",
#skin/melanoma
                                 "C440", "C441", "C442", "C443", "C444", "C445",
                                 "C446", "C447", "C448", "C449",
                                 
                                 #breast - nipple
                                 "C500",
                                 
                                 #vagina/vulva
                                 "C510", "C511", "C512", "C518", "C519", "C529",
                                 
                                 #penis
                                 "C600", "C601", "C602", "C608", "C609", "C639",
"C770",
"C771",
"C772",
"C773",
"C774",
"C775",
"C778",
"C779"),
SITE_TEXT = c(
"C00.0 External Lip: Upper NOS",
"C00.1 External Lip: Lower NOS",
"C00.2 External Lip: NOS",
"C00.3 Lip: Upper Mucosa",
"C00.4 Lip: Lower Mucosa",
"C00.5 Lip: Mucosa NOS",
"C00.6 Lip: Commissure",
"C00.8 Lip: Overlapping",
"C00.9 Lip NOS",
"C01.9 Tongue: Base NOS",
"C02.0 Tongue: Dorsal NOS",
"C02.1 Tongue: Border, Tip",
"C02.2 Tongue: Ventral NOS",
"C02.3 Tongue: Anterior NOS",
"C02.4 Lingual Tonsil",
"C02.8 Tongue: Overlapping",
"C02.9 Tongue: NOS",
"C03.0 Gum: Upper",
"C03.1 Gum: Lower",
"C03.9 Gum NOS",
"C04.0 Mouth: Anterior Floor",
"C04.1 Mouth: Lateral Floor",
"C04.8 Mouth: Overlapping Floor",
"C04.9 Floor of Mouth NOS",
"C05.0 Hard Palate",
"C05.1 Soft Palate NOS",
"C05.2 Uvula",
"C05.8 Palate: Overlapping",
"C05.9 Palate NOS",
"C06.0 Cheek Mucosa",
"C06.1 Mouth: Vestibule",
"C06.2 Retromolar Area",
"C06.8 Mouth: Other Overlapping",
"C06.9 Mouth NOS",
"C07.9 Parotid Gland",
  "C09.8 Tonsil: Overlapping",
  "C09.9 Tonsil NOS",
  "C11.1 Nasopharynx: Poster Wall", 
  "C14.2 Waldeyer Ring",
  "C30.0 Nasal Cavity",
  "C37.9 Thymus",
"C42.0 Blood",
  "C42.2 Spleen",
"C42.4 Hematopoietic NOS",
 #skin
"C44.0 Skin of lip, NOS",
"C44.1 Eyelid",
"C44.2 External ear",
"C44.3 Skin of ear and unspecified parts of face",
"C44.4 Skin of scalp and neck",
"C44.5 Skin of trunk",
"C44.6 Skin of upper limb and shoulder",
"C44.7 Skin of lower limb and hip",
"C44.8 Overlapping lesion of skin",
"C44.9 Skin, NOS", 
#breast
"C50.0 Nipple",
#vulva/vagina
"C51.0 Labium majus",
"C51.1 Labium minus",
"C51.2 Clitoris",
"C51.8 Overlapping lesion of vulva",
"C51.9 Vulva, NOS",
"C52.9 Vagina, NOS",
#penis
"C60.0 Prepuce",
"C60.1 Glans penis",
"C60.2 Body of penis",
"C60.8 Overlapping lesion of penis",
"C60.9 Penis",
"C63.2 Scrotum, NOS",
  "C77.0 Lymph Nodes: HeadFaceNeck",
  "C77.1 Intrathoracic Lymph Nodes",
  "C77.2 Intra-abdominal LymphNodes",
  "C77.3 Lymph Nodes of axilla or arm ",
  "C77.4 Lymph Nodes: Leg",
  "C77.5 Pelvic Lymph Nodes",
  "C77.8 Lymph Nodes: multiple region",
  "C77.9 Lymph Node NOS"))
dat <- merge(dat, prim_site_text, by = "PRIMARY_SITE", all.x = TRUE) 
rm(prim_site_text)
# convert numeric variables from character class to numeric class
num_vars <- c("AGE", "CROWFLY", "TUMOR_SIZE", "DX_STAGING_PROC_DAYS", "DX_RX_STARTED_DAYS", "DX_SURG_STARTED_DAYS",
              "DX_DEFSURG_STARTED_DAYS", "SURG_DISCHARGE_DAYS", "DX_RAD_STARTED_DAYS",  "RAD_REGIONAL_DOSE_CGY",
              "RAD_BOOST_DOSE_CGY", "RAD_ELAPSED_RX_DAYS", "DX_SYSTEMIC_STARTED_DAYS", "DX_CHEMO_STARTED_DAYS", 
              "DX_HORMONE_STARTED_DAYS", "DX_OTHER_STARTED_DAYS", "DX_LASTCONTACT_DEATH_MONTHS",
              "RAD_NUM_TREAT_VOL")
dat[num_vars] <- lapply(dat[num_vars], as.numeric)
# convert factor variables from character class to factor class
vars <- names(dat)
fact_vars <- vars[!(vars %in% num_vars)] # basically all of the non-numerics
dat[fact_vars] <- lapply(dat[fact_vars], as.character)
dat[fact_vars] <- lapply(dat[fact_vars], as.factor)
dat <- dat %>%
        mutate(FACILITY_TYPE_F = fct_recode(FACILITY_TYPE_CD,
                                            "Community Cancer Program" = "1",
                                            "Comprehensive Comm Ca Program" = "2",
                                            "Academic/Research Program" = "3",
                                            "Integrated Network Ca Program" = "4",
                                            "Other" = "9")) %>%
        mutate(FACILITY_LOCATION_F = fct_recode(FACILITY_LOCATION_CD,
                                            "New England" = "1",
                                            "Middle Atlantic" = "2",
                                            "South Atlantic" = "3",
                                            "East North Central" = "4",
                                            "East South Central" = "5",
                                            "West North Central" = "6",
                                            "West South Central" = "7",
                                            "Mountain" = "8",
                                            "Pacific" = "9",
                                            "out of US" = "0")) %>%
        mutate(FACILITY_GEOGRAPHY = fct_collapse(FACILITY_LOCATION_CD,
                                                 "Northeast" = c("1", "2"),
                                                 "South" = c("3", "7"),
                                                 "Midwest" = c("4", "5", "6"),
                                                 "West" = c("8", "9"))) %>%
        mutate(AGE_F = cut(AGE, c(0, 54, 64, 74, 100))) %>%
        mutate(AGE_40 = cut(AGE, c(0, 40, 100))) %>%
        mutate(SEX_F = fct_recode(SEX,
                                "Male" = "1",
                                "Female" = "2")) %>%
        mutate(RACE_F = fct_collapse(RACE,
                                "White" = c("01"),
                                "Black" = c("02"),
                                "Asian" = c("04", "05", "06", "07", "08", "10", "11", "12", "13", "14", "15",
                                            "16", "17", "20", "21", "22", "25", "26", "27", "28", "30", "31",
                                            "32", "96", "97"),
                                "Other/Unk" = c("03", "98", "99"))) %>%
        mutate(HISPANIC = fct_collapse(SPANISH_HISPANIC_ORIGIN,
                                       "Yes" = c("1", "2", "3", "4", "5", "6", "7", "8"),
                                       "No" = c("0"),
                                       "Unknown" = c("9"))) %>%
        mutate(INSURANCE_F = fct_recode(INSURANCE_STATUS,
                                         "None" = "0",
                                         "Private" = "1",
                                         "Medicaid" = "2",
                                         "Medicare" = "3",
                                         "Other Government" = "4",
                                         "Unknown" = "9")) %>%
        mutate(INSURANCE_F = fct_relevel(INSURANCE_F,
                                         "Private")) %>%
        mutate(INCOME_F = fct_recode(MED_INC_QUAR_12,
                                      "Less than $38,000" = "1",
                                      "$38,000 - $47,999" = "2",
                                      "$48,000 - $62,999" = "3",
                                      "$63,000 +" = "4")) %>%
        mutate(EDUCATION_F = fct_recode(NO_HSD_QUAR_12,
                                        "21% or more" = "1",
                                        "13 - 20.9%" = "2",
                                        "7 - 12.9%" = "3",
                                        "Less than 7%" = "4")) %>%
        mutate(U_R_F = fct_collapse(UR_CD_13,
                                    "Metro" = c("1", "2", "3"),
                                    "Urban" = c("4", "5", "6", "7"),
                                    "Rural" = c("8", "9"))) %>%
        mutate(CLASS_OF_CASE_F = fct_collapse(CLASS_OF_CASE,
                                              All_Part_Prim = c("10", "11", "12", "13",
                                                                "14", "20", "21", "22"),
                                              Other_Facility = c("00"))) %>%
        mutate(GRADE_F = fct_recode(GRADE,
                                  "Gr I: Well Diff" = "1",
                                  "Gr II: Mod Diff" = "2",
                                  "Gr III: Poor Diff" = "3",
                                  "Gr IV: Undiff/Anaplastic" = "4",
                                  "NA/Unkown" = "9")) %>%
        mutate(HISTOLOGY_F = fct_infreq(HISTOLOGY)) %>%
        mutate(HISTOLOGY_F = factor(HISTOLOGY_F)) %>%
        mutate(HISTOLOGY_F_LIM = fct_lump(HISTOLOGY_F, prop = 0.05)) %>%
        mutate(TNM_CLIN_T = fct_recode(TNM_CLIN_T,
                                       "N_A" = "88")) %>%
        mutate(TNM_CLIN_T = fct_relevel(TNM_CLIN_T,
                                        "1")) %>%
        mutate(TNM_CLIN_N = fct_recode(TNM_CLIN_N,
                                       "N_A" = "88")) %>%
        mutate(TNM_CLIN_M = fct_recode(TNM_CLIN_M,
                                       "N_A" = "88")) %>%
        mutate(TNM_PATH_T = fct_recode(TNM_PATH_T,
                                       "N_A" = "88")) %>%
        mutate(TNM_PATH_T = fct_relevel(TNM_PATH_T,
                                        "1")) %>%
        mutate(TNM_PATH_N = fct_recode(TNM_PATH_N,
                                       "N_A" = "88")) %>%
        mutate(TNM_PATH_M = fct_recode(TNM_PATH_M,
                                       "N_A" = "88")) %>%
        mutate(TNM_CLIN_STAGE_GROUP = fct_recode(TNM_CLIN_STAGE_GROUP,
                                       "N_A" = "88")) %>%
        mutate(TNM_PATH_STAGE_GROUP = fct_recode(TNM_PATH_STAGE_GROUP,
                                       "N_A" = "88")) %>%
        mutate(MARGINS = fct_recode(RX_SUMM_SURGICAL_MARGINS,
                                    "No Residual" = "0",
                                    "Residual, NOS" = "1",
                                    "Microscopic Resid" = "2",
                                    "Macroscopic Resid" = "3",
                                    "Not evaluable" = "7",
                                    "No surg" = "8",
                                    "Unknown" = "9")) %>%
        mutate(MARGINS_YN = fct_collapse(RX_SUMM_SURGICAL_MARGINS,
                                         "Yes" = c("1", "2", "3"),
                                         "No" = c("0"),
                                         "No surg/Unk/NA" = c("7", "8", "9"))) %>%
        mutate(READM_HOSP_30_DAYS_F = fct_recode(READM_HOSP_30_DAYS,
                                                 "No_Surg_or_No_Readmit" = "0",
                                                 "Unplan_Readmit_Same" = "1",
                                                 "Plan_Readmit_Same" = "2",
                                                 "PlanUnplan_Same" = "3",
                                                 "Unknown" = "4")) %>%
        mutate(RX_SUMM_RADIATION_F = fct_recode(RX_SUMM_RADIATION,
                                                "None" = "0",
                                                "Beam Radiation" = "1",
                                                "Radioactive Implants" = "2",
                                                "Radioisotopes" = "3",
                                                "Beam + Imp or Isotopes" = "4",
                                                "Radiation, NOS" = "5",
                                                "Unknown" = "9")) %>%
        mutate(PUF_30_DAY_MORT_CD_F = fct_recode(PUF_30_DAY_MORT_CD,
                                                 "Alive_30" = "0",
                                                 "Dead_30" = "1",
                                                 "Unknown" = "9")) %>%
        mutate(PUF_90_DAY_MORT_CD_F = fct_recode(PUF_90_DAY_MORT_CD,
                                                 "Alive_90" = "0",
                                                 "Dead_90" = "1",
                                                 "Unknown" = "9")) %>%
        mutate(LYMPH_VASCULAR_INVASION_F = fct_recode(LYMPH_VASCULAR_INVASION,
                                                      "Neg_LymphVasc_Inv" = "0",
                                                      "Pos_LumphVasc_Inv" = "1",
                                                      "N_A" = "8",
                                                      "Unknown" = "9")) %>%
        mutate(RX_HOSP_SURG_APPR_2010_F = fct_recode(RX_HOSP_SURG_APPR_2010,
                                                     "No_Surg" = "0",
                                                     "Robot_Assist" = "1",
                                                     "Robot_to_Open" = "2",
                                                     "Endo_Lap" = "3",
                                                     "Endo_Lap_to_Open" = "4",
                                                     "Open_Unknown" = "5",
                                                     "Unknown" = "9")) %>%
        mutate(All = "All") %>%
        mutate(All = factor(All)) %>%
        mutate(REASON_FOR_NO_SURGERY_F = fct_recode(REASON_FOR_NO_SURGERY,
                                                    "Surg performed" = "0",
                                                    "Surg not recommended" = "1",
                                                    "No surg due to pt factors" = "2",
                                                    "No surg, pt died" = "5",
                                                    "Surg rec, not done" = "6",
                                                    "Surg rec, pt refused" = "7",
                                                    "Surg rec, unk if done" = "8",
                                                    "Unknown" = "9")) %>%
        mutate(SURGERY_YN = ifelse(REASON_FOR_NO_SURGERY == "0",
                                   "Yes",
                                   ifelse(REASON_FOR_NO_SURGERY == "9",
                                          "Ukn",
                                          "No"))) %>%
        mutate(SURG_TF = case_when(SURGERY_YN == "Yes" ~ TRUE,
                             SURGERY_YN == "No" ~ FALSE,
                             TRUE ~ NA))  %>%
        mutate(REASON_FOR_NO_RADIATION_F = fct_recode(REASON_FOR_NO_RADIATION,
                                                    "Rad performed" = "0",
                                                    "Rad not recommended" = "1",
                                                    "No Rad due to pt factors" = "2",
                                                    "No Rad, pt died" = "5",
                                                    "Rad rec, not done" = "6",
                                                    "Rad rec, pt refused" = "7",
                                                    "Rad rec, unk if done" = "8",
                                                    "Unknown" = "9")) %>%
        mutate(RADIATION_YN = ifelse(REASON_FOR_NO_RADIATION == "0",
                                   "Yes",
                                   ifelse(REASON_FOR_NO_RADIATION == "9",
                                          NA,
                                          "No"))) %>%
        mutate(SURGRAD_SEQ_F = fct_recode(RX_SUMM_SURGRAD_SEQ,
                                                   "None or Surg or Rad" = "0",
                                                   "Rad before Surg" = "2",
                                                   "Surg before Rad" = "3",
                                                   "Rad before and after Surg" = "4",
                                                   "Intraop Rad" = "5",
                                                   "Intraop Rad plus other" = "6",
                                                   "Unknown" = "9")) %>%
        mutate(SURG_RAD_SEQ = ifelse(SURGERY_YN == "Yes" & RX_SUMM_SURGRAD_SEQ == "0",
                                     "Surg Alone",
                                     ifelse(RADIATION_YN == "Yes" & RX_SUMM_SURGRAD_SEQ == "0",
                                            "Rad Alone",
                                            ifelse(SURGERY_YN == "No" & RADIATION_YN == "No" & RX_SUMM_SURGRAD_SEQ == "0",
                                                   "No Treatment",
                                                   ifelse(RX_SUMM_SURGRAD_SEQ == "2",
                                                          "Rad then Surg",
                                                          ifelse(RX_SUMM_SURGRAD_SEQ == "3",
                                                                 "Surg then Rad",
                                                                 ifelse(RX_SUMM_SURGRAD_SEQ == "4",
                                                                        "Rad before and after Surg",
                                                                        "Other"))))))) %>%
        mutate(SURG_RAD_SEQ = fct_relevel(SURG_RAD_SEQ,
                                          "Surg Alone",
                                          "Surg then Rad",
                                          "Rad Alone")) %>%
        mutate(CHEMO_YN = fct_collapse(RX_SUMM_CHEMO,
                                       "No" = c("00", "82", "85", "86", "87"),
                                       "Yes" = c("01", "02", "03"),
                                       "Ukn" = c("88", "99"))) %>%
        mutate(IMMUNO_YN = fct_collapse(RX_SUMM_IMMUNOTHERAPY,
                                       "No" = c("00", "82", "85", "86", "87"),
                                       "Yes" = c("01"),
                                       "Ukn" = c("88", "99"))) %>%
        mutate(SURG_RAD_SEQ_C = ifelse(SURGERY_YN == "Yes" & RX_SUMM_SURGRAD_SEQ == "0" & CHEMO_YN == "No",
                                     "Surg, No rad, No Chemo",
                                     ifelse(RADIATION_YN == "Yes" & RX_SUMM_SURGRAD_SEQ == "0" & CHEMO_YN == "No",
                                            "Rad, No Surg, No Chemo",
                                            ifelse(SURGERY_YN == "No" & RADIATION_YN == "No" & RX_SUMM_SURGRAD_SEQ == "0" & CHEMO_YN == "No",
                                                   "No Surg, No Rad, No Chemo",
                                                   ifelse(RX_SUMM_SURGRAD_SEQ == "2" & CHEMO_YN == "No",
                                                          "Rad then Surg, No Chemo",
                                                          ifelse(RX_SUMM_SURGRAD_SEQ == "3" & CHEMO_YN == "No",
                                                                 "Surg then Rad, No Chemo",
                                                                 ifelse(RX_SUMM_SURGRAD_SEQ == "4" & CHEMO_YN == "No",
                                                                        "Rad before and after Surg, No Chemo",
                                ifelse(SURGERY_YN == "Yes" & RX_SUMM_SURGRAD_SEQ == "0" & CHEMO_YN == "Yes",
                                       "Surg, No rad, Yes Chemo",
                                       ifelse(RADIATION_YN == "Yes" & RX_SUMM_SURGRAD_SEQ == "0" & CHEMO_YN == "Yes",
                                              "Rad, No Surg, Yes Chemo",
                                              ifelse(SURGERY_YN == "No" & RADIATION_YN == "No" & RX_SUMM_SURGRAD_SEQ == "0" & CHEMO_YN == "Yes",
                                                     "No Surg, No Rad, Yes Chemo",
                                                     ifelse(RX_SUMM_SURGRAD_SEQ == "2" & CHEMO_YN == "Yes",
                                                            "Rad then Surg, Yes Chemo",
                                                            ifelse(RX_SUMM_SURGRAD_SEQ == "3" & CHEMO_YN == "Yes",
                                                                   "Surg then Rad, Yes Chemo",
                                                                   ifelse(RX_SUMM_SURGRAD_SEQ == "4" & CHEMO_YN == "Yes",
                                                                          "Rad before and after Surg, Yes Chemo",
                                                                          "Other"))))))))))))) %>%
        mutate(SURG_RAD_SEQ_C = fct_infreq(SURG_RAD_SEQ_C)) %>%
        mutate(T_SIZE = as.numeric(TUMOR_SIZE)) %>%
        mutate(T_SIZE = ifelse(T_SIZE == 0,
                                "No Tumor",
                                ifelse(T_SIZE > 0 & T_SIZE < 10 | T_SIZE == 991,
                                       "< 1 cm",
                                       ifelse(T_SIZE >= 10 & T_SIZE < 20 | T_SIZE == 992,
                                              "1-2 cm",
                                              ifelse(T_SIZE >= 20 & T_SIZE < 30 | T_SIZE == 993,
                                                     "2-3 cm",
                                                     ifelse(T_SIZE >= 30 & T_SIZE < 40 | T_SIZE == 994,
                                                            "3-4 cm",
                                                            ifelse(T_SIZE >= 40 & T_SIZE < 50 | T_SIZE == 995,
                                                                   "4-5 cm",
                                                                   ifelse(T_SIZE >= 50 & T_SIZE < 60 | T_SIZE == 996,
                                                                          "5-6 cm",
                                                                          ifelse(T_SIZE >= 60 & T_SIZE <= 987 |
                                                                                         T_SIZE == 980 | T_SIZE == 989 |
                                                                                         T_SIZE == 997,
                                                                          ">6 cm",
                                                                          ifelse(T_SIZE == 988 | T_SIZE == 999,
                                                                                 "NA_unk",
                                                                                 "Microscopic focus")))))))))) %>%
        mutate(T_SIZE = factor(T_SIZE)) %>%
        mutate(T_SIZE = fct_relevel(T_SIZE,
                                     "No Tumor", "Microscopic focus", "< 1 cm", "1-2 cm", "2-3 cm", "3-4 cm",
                                       "4-5 cm", "5-6 cm", ">6 cm", "NA_unk")) %>%
        mutate(mets_at_dx = case_when(CS_METS_DX_LUNG == "1" ~ "Lung",
                                      CS_METS_DX_BONE == "1" ~ "Bone",
                                      CS_METS_DX_BRAIN == "1" ~ "Brain",
                                      CS_METS_DX_LIVER == "1" ~ "Liver",
                                      TRUE ~ "None/Other/Unk/NA")) %>%
        mutate(MEDICAID_EXPN_CODE = fct_recode(MEDICAID_EXPN_CODE,
                                               "Non-Expansion State" = "0",
                                               "Jan 2014 Expansion States" = "1",
                                               "Early Expansion States (2010-13)" = "2",
                                               "Late Expansion States (> Jan 2014)" = "3",
                                               "Suppressed for Ages 0 - 39" = "9"))  %>%
        mutate(EXPN_GROUP =  case_when(MEDICAID_EXPN_CODE  %in% c("Jan 2014 Expansion States") & 
                                         YEAR_OF_DIAGNOSIS %in% c("2014", "2015") ~ "Post-Expansion",
                                       
                                       MEDICAID_EXPN_CODE  %in% c("Jan 2014 Expansion States") & 
                                         YEAR_OF_DIAGNOSIS %in% 
                                          c("2004", "2005", "2006", "2007", "2008", 
                                            "2009", "2010", "2011", "2012", "2013") ~ "Pre-Expansion",
               
                                       MEDICAID_EXPN_CODE  %in% c("Early Expansion States (2010-13)") & 
                                         YEAR_OF_DIAGNOSIS %in% c("2010", "2011", "2012", "2013", "2014", "2015") ~ "Post-Expansion",
                                       
                                        MEDICAID_EXPN_CODE  %in% c("Early Expansion States (2010-13)") & 
                                         YEAR_OF_DIAGNOSIS %in% c("2004", "2005", "2006", "2007", "2008", "2009") ~ "Pre-Expansion",
                                       MEDICAID_EXPN_CODE %in% c("Non-Expansion State") ~ "Pre-Expansion",
                                       MEDICAID_EXPN_CODE %in% c("Late Expansion States (> Jan 2014)") ~ "Pre-Expansion",
                    
                                       MEDICAID_EXPN_CODE %in% c("Late Expansion States (> Jan 2014)") & 
                                        YEAR_OF_DIAGNOSIS %in% c("2014", "2015") ~ "Exclude",
                                       
                                       MEDICAID_EXPN_CODE == "Suppressed for Ages 0 - 39" ~ "Exclude")) %>%
  
  mutate(pre_2014 = YEAR_OF_DIAGNOSIS %in% c("2004", "2005", "2006", "2007", "2008", 
                                            "2009", "2010", "2011", "2012", "2013")) %>%
  
  mutate(mets_at_dx_F = ifelse(mets_at_dx == "None/Other/Unk/NA", FALSE, TRUE)) %>% 
  
  mutate(Tx_YN = ifelse(SURG_RAD_SEQ == "No Treatment" & CHEMO_YN == "No" & 
                          IMMUNO_YN == "No", FALSE, 
                        ifelse(CHEMO_YN == "Ukn", NA, 
                               TRUE)))
fact_vars_2 <- c("FACILITY_TYPE_F", "FACILITY_LOCATION_F", "AGE_F", "SEX_F", "RACE_F",
                 "HISPANIC", "INSURANCE_F", "INCOME_F", "EDUCATION_F", "U_R_F",
                 "CDCC_TOTAL_BEST", "CLASS_OF_CASE_F", "YEAR_OF_DIAGNOSIS", "PRIMARY_SITE", "HISTOLOGY",
                 "BEHAVIOR", "GRADE_F", "TNM_CLIN_T", "TNM_CLIN_N", "TNM_CLIN_M",
                 "TNM_CLIN_STAGE_GROUP", "TNM_PATH_T", "TNM_PATH_N", "TNM_PATH_M", "TNM_PATH_STAGE_GROUP",
                 "MARGINS", "READM_HOSP_30_DAYS_F", "RX_SUMM_RADIATION_F", "PUF_30_DAY_MORT_CD_F",
                 "PUF_90_DAY_MORT_CD_F", "LYMPH_VASCULAR_INVASION_F", "RX_HOSP_SURG_APPR_2010_F", "mets_at_dx")
dat <- dat %>%
        mutate_at(fact_vars_2, funs(factor(.)))

Extract data of interest

# EMPD
site_code <- c(
  #lip  
  "C000", "C001", "C002", "C003", "C004", "C005","C006", "C008","C009",
                                  
                                 
#skin/melanoma
  "C440", "C441", "C442", "C443", "C444", "C445", "C446", "C447", "C448", "C449",
                                 
                                 
#vagina/vulva
  "C510", "C511", "C512", "C518", "C519", "C529",
                                 
#penis
 "C600", "C601", "C602", "C608", "C609", "C639")
histo_code <- c("8542")
behavior_code <- c("3")
data <- dat %>%
        filter(BEHAVIOR %in% behavior_code) %>%
        filter(PRIMARY_SITE %in% site_code) %>%
        filter(HISTOLOGY %in% histo_code) %>%
        filter(is.na(PUF_VITAL_STATUS) == FALSE) %>%
        filter(is.na(DX_LASTCONTACT_DEATH_MONTHS) == FALSE) %>%
        filter(SEQUENCE_NUMBER == "00") 
no_Excludes <- as.data.frame(data %>% 
                               filter(EXPN_GROUP != "Exclude") 
                             %>% droplevels())
#file_path <- c("/Users/beastatlife/Google Drive/Penn/Research/Barbieri/NCDB")
#save(data,
#      file = paste0(file_path, "/EMPD_data.Rda"))
#load("EMPD_data.Rda")

Data including skin tumors was obtained from the NCBD on October 7, 2019. Cases that were included in this analysis were those with:

  1. Site codes: C000, C001, C002, C003, C004, C005, C006, C008, C009, C440, C441, C442, C443, C444, C445, C446, C447, C448, C449, C510, C511, C512, C518, C519, C529, C600, C601, C602, C608, C609, C639
  2. Histology codes: 8542
  3. Behavior codes: 3

Patients were excluded if they didn’t have values for either follow up or vital status.

Patients were excluded if they had surgery to a distant site using RX_SUMM_SURG_OTH_REGDIS. This was done to avoid confounding of different surgical procedures. We are only interested in surgery at the primary site. These distant site surgeries were being counted in the surgery/radiation sequence and thus to simplify analysis they were removed.

data %>%
        CreateTableOne(data = .,
                     vars = c("RX_SUMM_SURG_OTH_REGDIS"),
                     includeNA = TRUE) %>%
        print(.,
              showAllLevels = TRUE)
                             
                              level Overall     
  n                                 1399        
  RX_SUMM_SURG_OTH_REGDIS (%) 0     1360 (97.2) 
                              1        4 ( 0.3) 
                              2       16 ( 1.1) 
                              3        1 ( 0.1) 
                              4        0 ( 0.0) 
                              5        0 ( 0.0) 
                              9       18 ( 1.3) 
data <- data %>%
        filter(RX_SUMM_SURG_OTH_REGDIS == "0") 

Race was grouped as white, black, asian, other/unknown Stage was grouped into 0, I, II, III, IV, NA_Unknown, stage 0 was removed Whether surgery was performed was based on the REASON_FOR_NO_SURGERY variable. The SURGERY_YN variable was classified as ‘Yes’, ‘No’, or ‘Unknown’.

Whether radiation was performed was based on the REASON_FOR_NO_RADIATION variable. The RADIATION_YN variable was classified as ‘Yes’, ‘No’, or ‘Unknown’.

##Table of variables for all cases:

p_table(data,
        vars = c("FACILITY_TYPE_F", "FACILITY_LOCATION_F", "FACILITY_GEOGRAPHY",  "AGE", "AGE_F", "AGE_40",
                 "SEX_F", "RACE_F", "HISPANIC", "INSURANCE_F", 
                 "INCOME_F", "EDUCATION_F", "U_R_F", "CROWFLY", "CDCC_TOTAL_BEST",
                 "SITE_TEXT",  "BEHAVIOR", "GRADE_F",
                 "DX_STAGING_PROC_DAYS", "TNM_CLIN_T", "TNM_CLIN_N", "TNM_CLIN_M",
                 "TNM_CLIN_STAGE_GROUP", "TNM_PATH_T", "TNM_PATH_N", "TNM_PATH_M",
                 "TNM_PATH_STAGE_GROUP", "DX_RX_STARTED_DAYS", "DX_SURG_STARTED_DAYS",
                 "DX_DEFSURG_STARTED_DAYS", "MARGINS", "MARGINS_YN", "SURG_DISCHARGE_DAYS",
                 "READM_HOSP_30_DAYS_F", "RX_SUMM_RADIATION_F", "PUF_30_DAY_MORT_CD_F",
                 "PUF_90_DAY_MORT_CD_F", "DX_LASTCONTACT_DEATH_MONTHS", 
                 "LYMPH_VASCULAR_INVASION_F", "RX_HOSP_SURG_APPR_2010_F", "SURG_RAD_SEQ",
                 "SURG_RAD_SEQ_C", "SURGERY_YN", "RADIATION_YN", "CHEMO_YN", 
                 "IMMUNO_YN", "Tx_YN", "mets_at_dx",
                 "MEDICAID_EXPN_CODE", "EXPN_GROUP"))
level Overall
n 1360
FACILITY_TYPE_F (%) Community Cancer Program 31 ( 2.3)
Comprehensive Comm Ca Program 462 ( 34.0)
Academic/Research Program 654 ( 48.1)
Integrated Network Ca Program 199 ( 14.6)
NA 14 ( 1.0)
FACILITY_LOCATION_F (%) New England 59 ( 4.3)
Middle Atlantic 231 ( 17.0)
South Atlantic 255 ( 18.8)
East North Central 222 ( 16.3)
East South Central 89 ( 6.5)
West North Central 141 ( 10.4)
West South Central 99 ( 7.3)
Mountain 79 ( 5.8)
Pacific 171 ( 12.6)
NA 14 ( 1.0)
FACILITY_GEOGRAPHY (%) Northeast 290 ( 21.3)
South 354 ( 26.0)
Midwest 452 ( 33.2)
West 250 ( 18.4)
NA 14 ( 1.0)
AGE (mean (sd)) 70.52 (11.87)
AGE_F (%) (0,54] 144 ( 10.6)
(54,64] 254 ( 18.7)
(64,74] 421 ( 31.0)
(74,100] 541 ( 39.8)
AGE_40 (%) (0,40] 18 ( 1.3)
(40,100] 1342 ( 98.7)
SEX_F (%) Male 261 ( 19.2)
Female 1099 ( 80.8)
RACE_F (%) White 1229 ( 90.4)
Black 25 ( 1.8)
Other/Unk 35 ( 2.6)
Asian 71 ( 5.2)
HISPANIC (%) No 1234 ( 90.7)
Yes 52 ( 3.8)
Unknown 74 ( 5.4)
INSURANCE_F (%) Private 453 ( 33.3)
None 23 ( 1.7)
Medicaid 22 ( 1.6)
Medicare 838 ( 61.6)
Other Government 8 ( 0.6)
Unknown 16 ( 1.2)
INCOME_F (%) Less than $38,000 165 ( 12.1)
$38,000 - $47,999 326 ( 24.0)
$48,000 - $62,999 365 ( 26.8)
$63,000 + 499 ( 36.7)
NA 5 ( 0.4)
EDUCATION_F (%) 21% or more 167 ( 12.3)
13 - 20.9% 320 ( 23.5)
7 - 12.9% 465 ( 34.2)
Less than 7% 403 ( 29.6)
NA 5 ( 0.4)
U_R_F (%) Metro 1106 ( 81.3)
Urban 187 ( 13.8)
Rural 27 ( 2.0)
NA 40 ( 2.9)
CROWFLY (mean (sd)) 41.15 (124.43)
CDCC_TOTAL_BEST (%) 0 1106 ( 81.3)
1 198 ( 14.6)
2 40 ( 2.9)
3 16 ( 1.2)
SITE_TEXT (%) C00.0 External Lip: Upper NOS 0 ( 0.0)
C00.1 External Lip: Lower NOS 0 ( 0.0)
C00.2 External Lip: NOS 0 ( 0.0)
C00.3 Lip: Upper Mucosa 0 ( 0.0)
C00.4 Lip: Lower Mucosa 0 ( 0.0)
C00.5 Lip: Mucosa NOS 0 ( 0.0)
C00.6 Lip: Commissure 0 ( 0.0)
C00.8 Lip: Overlapping 0 ( 0.0)
C00.9 Lip NOS 0 ( 0.0)
C01.9 Tongue: Base NOS 0 ( 0.0)
C02.0 Tongue: Dorsal NOS 0 ( 0.0)
C02.1 Tongue: Border, Tip 0 ( 0.0)
C02.2 Tongue: Ventral NOS 0 ( 0.0)
C02.3 Tongue: Anterior NOS 0 ( 0.0)
C02.4 Lingual Tonsil 0 ( 0.0)
C02.8 Tongue: Overlapping 0 ( 0.0)
C02.9 Tongue: NOS 0 ( 0.0)
C03.0 Gum: Upper 0 ( 0.0)
C03.1 Gum: Lower 0 ( 0.0)
C03.9 Gum NOS 0 ( 0.0)
C04.0 Mouth: Anterior Floor 0 ( 0.0)
C04.1 Mouth: Lateral Floor 0 ( 0.0)
C04.9 Floor of Mouth NOS 0 ( 0.0)
C05.0 Hard Palate 0 ( 0.0)
C05.1 Soft Palate NOS 0 ( 0.0)
C05.2 Uvula 0 ( 0.0)
C05.8 Palate: Overlapping 0 ( 0.0)
C05.9 Palate NOS 0 ( 0.0)
C06.0 Cheek Mucosa 0 ( 0.0)
C06.1 Mouth: Vestibule 0 ( 0.0)
C06.2 Retromolar Area 0 ( 0.0)
C06.8 Mouth: Other Overlapping 0 ( 0.0)
C06.9 Mouth NOS 0 ( 0.0)
C07.9 Parotid Gland 0 ( 0.0)
C09.8 Tonsil: Overlapping 0 ( 0.0)
C09.9 Tonsil NOS 0 ( 0.0)
C11.1 Nasopharynx: Poster Wall 0 ( 0.0)
C14.2 Waldeyer Ring 0 ( 0.0)
C30.0 Nasal Cavity 0 ( 0.0)
C37.9 Thymus 0 ( 0.0)
C42.0 Blood 0 ( 0.0)
C42.2 Spleen 0 ( 0.0)
C42.4 Hematopoietic NOS 0 ( 0.0)
C44.0 Skin of lip, NOS 0 ( 0.0)
C44.1 Eyelid 0 ( 0.0)
C44.2 External ear 2 ( 0.1)
C44.3 Skin of ear and unspecified parts of face 2 ( 0.1)
C44.4 Skin of scalp and neck 3 ( 0.2)
C44.5 Skin of trunk 287 ( 21.1)
C44.6 Skin of upper limb and shoulder 7 ( 0.5)
C44.7 Skin of lower limb and hip 12 ( 0.9)
C44.8 Overlapping lesion of skin 11 ( 0.8)
C44.9 Skin, NOS 19 ( 1.4)
C50.0 Nipple 0 ( 0.0)
C51.0 Labium majus 92 ( 6.8)
C51.1 Labium minus 15 ( 1.1)
C51.2 Clitoris 2 ( 0.1)
C51.8 Overlapping lesion of vulva 60 ( 4.4)
C51.9 Vulva, NOS 822 ( 60.4)
C52.9 Vagina, NOS 1 ( 0.1)
C60.0 Prepuce 0 ( 0.0)
C60.1 Glans penis 0 ( 0.0)
C60.2 Body of penis 1 ( 0.1)
C60.8 Overlapping lesion of penis 2 ( 0.1)
C60.9 Penis 22 ( 1.6)
C63.2 Scrotum, NOS 0 ( 0.0)
C77.0 Lymph Nodes: HeadFaceNeck 0 ( 0.0)
C77.1 Intrathoracic Lymph Nodes 0 ( 0.0)
C77.2 Intra-abdominal LymphNodes 0 ( 0.0)
C77.3 Lymph Nodes of axilla or arm 0 ( 0.0)
C77.4 Lymph Nodes: Leg 0 ( 0.0)
C77.5 Pelvic Lymph Nodes 0 ( 0.0)
C77.8 Lymph Nodes: multiple region 0 ( 0.0)
C77.9 Lymph Node NOS 0 ( 0.0)
BEHAVIOR (%) 2 0 ( 0.0)
3 1360 (100.0)
GRADE_F (%) Gr I: Well Diff 38 ( 2.8)
Gr II: Mod Diff 30 ( 2.2)
Gr III: Poor Diff 36 ( 2.6)
Gr IV: Undiff/Anaplastic 0 ( 0.0)
5 0 ( 0.0)
6 0 ( 0.0)
7 0 ( 0.0)
8 0 ( 0.0)
NA/Unkown 1256 ( 92.4)
DX_STAGING_PROC_DAYS (mean (sd)) 1.96 (14.28)
TNM_CLIN_T (%) N_A 1 ( 0.1)
c0 6 ( 0.4)
c1 139 ( 10.2)
c1A 148 ( 10.9)
c1B 138 ( 10.1)
c1C 0 ( 0.0)
c1MI 0 ( 0.0)
c2 176 ( 12.9)
c2A 0 ( 0.0)
c2B 0 ( 0.0)
c2C 0 ( 0.0)
c2D 0 ( 0.0)
c3 25 ( 1.8)
c3A 0 ( 0.0)
c3B 0 ( 0.0)
c4 1 ( 0.1)
c4A 0 ( 0.0)
c4B 0 ( 0.0)
c4C 0 ( 0.0)
c4D 0 ( 0.0)
cX 625 ( 46.0)
pA 0 ( 0.0)
pIS 28 ( 2.1)
NA 73 ( 5.4)
TNM_CLIN_N (%) N_A 1 ( 0.1)
c0 872 ( 64.1)
c1 4 ( 0.3)
c1A 0 ( 0.0)
c1B 0 ( 0.0)
c2 4 ( 0.3)
c2A 0 ( 0.0)
c2B 3 ( 0.2)
c2C 1 ( 0.1)
c3 0 ( 0.0)
c3A 0 ( 0.0)
c3B 0 ( 0.0)
c3C 0 ( 0.0)
c4 0 ( 0.0)
cX 412 ( 30.3)
NA 63 ( 4.6)
TNM_CLIN_M (%) N_A 1 ( 0.1)
c0 1248 ( 91.8)
c0I+ 0 ( 0.0)
c1 11 ( 0.8)
c1A 0 ( 0.0)
c1B 0 ( 0.0)
c1C 0 ( 0.0)
NA 100 ( 7.4)
TNM_CLIN_STAGE_GROUP (%) 0 64 ( 4.7)
1 196 ( 14.4)
1A 109 ( 8.0)
1B 119 ( 8.8)
1C 0 ( 0.0)
2 184 ( 13.5)
2A 0 ( 0.0)
2B 0 ( 0.0)
2C 0 ( 0.0)
3 15 ( 1.1)
3A 0 ( 0.0)
3B 1 ( 0.1)
3C 0 ( 0.0)
4 9 ( 0.7)
4A 2 ( 0.1)
4A1 0 ( 0.0)
4A2 0 ( 0.0)
4B 5 ( 0.4)
4C 0 ( 0.0)
N_A 1 ( 0.1)
99 655 ( 48.2)
TNM_PATH_T (%) N_A 1 ( 0.1)
p0 9 ( 0.7)
p1 98 ( 7.2)
p1A 147 ( 10.8)
p1B 157 ( 11.5)
p1C 0 ( 0.0)
p1MI 0 ( 0.0)
p2 144 ( 10.6)
p2A 0 ( 0.0)
p2B 0 ( 0.0)
p2C 0 ( 0.0)
p2D 0 ( 0.0)
p3 22 ( 1.6)
p3A 0 ( 0.0)
p3B 0 ( 0.0)
p4 1 ( 0.1)
p4A 0 ( 0.0)
p4B 0 ( 0.0)
p4C 0 ( 0.0)
p4D 0 ( 0.0)
pA 0 ( 0.0)
pIS 23 ( 1.7)
pX 601 ( 44.2)
NA 157 ( 11.5)
TNM_PATH_N (%) N_A 1 ( 0.1)
p0 282 ( 20.7)
p0I- 0 ( 0.0)
p0I+ 0 ( 0.0)
p0M- 0 ( 0.0)
p0M+ 0 ( 0.0)
p1 9 ( 0.7)
p1A 1 ( 0.1)
p1B 1 ( 0.1)
p1C 0 ( 0.0)
p1MI 0 ( 0.0)
p2 1 ( 0.1)
p2A 0 ( 0.0)
p2B 1 ( 0.1)
p2C 6 ( 0.4)
p3 1 ( 0.1)
p3A 0 ( 0.0)
p3B 0 ( 0.0)
p3C 0 ( 0.0)
p4 0 ( 0.0)
pX 844 ( 62.1)
NA 213 ( 15.7)
TNM_PATH_M (%) N_A 1 ( 0.1)
p0 0 ( 0.0)
p1 3 ( 0.2)
p1A 0 ( 0.0)
p1B 0 ( 0.0)
p1C 0 ( 0.0)
pX 622 ( 45.7)
NA 734 ( 54.0)
TNM_PATH_STAGE_GROUP (%) 0 58 ( 4.3)
1 107 ( 7.9)
1A 104 ( 7.6)
1B 92 ( 6.8)
1C 0 ( 0.0)
2 120 ( 8.8)
2A 0 ( 0.0)
2B 0 ( 0.0)
2C 0 ( 0.0)
3 26 ( 1.9)
3A 2 ( 0.1)
3B 0 ( 0.0)
3C 5 ( 0.4)
4 4 ( 0.3)
4A 5 ( 0.4)
4A1 0 ( 0.0)
4B 1 ( 0.1)
4C 0 ( 0.0)
N_A 1 ( 0.1)
99 756 ( 55.6)
NA 79 ( 5.8)
DX_RX_STARTED_DAYS (mean (sd)) 49.30 (133.55)
DX_SURG_STARTED_DAYS (mean (sd)) 46.72 (118.95)
DX_DEFSURG_STARTED_DAYS (mean (sd)) 58.37 (134.05)
MARGINS (%) No Residual 601 ( 44.2)
Residual, NOS 164 ( 12.1)
Microscopic Resid 351 ( 25.8)
Macroscopic Resid 17 ( 1.2)
Not evaluable 22 ( 1.6)
No surg 161 ( 11.8)
Unknown 44 ( 3.2)
MARGINS_YN (%) No 601 ( 44.2)
Yes 532 ( 39.1)
No surg/Unk/NA 227 ( 16.7)
SURG_DISCHARGE_DAYS (mean (sd)) 2.12 (6.93)
READM_HOSP_30_DAYS_F (%) No_Surg_or_No_Readmit 1292 ( 95.0)
Unplan_Readmit_Same 36 ( 2.6)
Plan_Readmit_Same 15 ( 1.1)
PlanUnplan_Same 1 ( 0.1)
9 16 ( 1.2)
RX_SUMM_RADIATION_F (%) None 1268 ( 93.2)
Beam Radiation 73 ( 5.4)
Radioactive Implants 1 ( 0.1)
Radioisotopes 0 ( 0.0)
Beam + Imp or Isotopes 1 ( 0.1)
Radiation, NOS 0 ( 0.0)
Unknown 17 ( 1.2)
PUF_30_DAY_MORT_CD_F (%) Alive_30 1156 ( 85.0)
Dead_30 5 ( 0.4)
Unknown 33 ( 2.4)
NA 166 ( 12.2)
PUF_90_DAY_MORT_CD_F (%) Alive_90 1134 ( 83.4)
Dead_90 10 ( 0.7)
Unknown 50 ( 3.7)
NA 166 ( 12.2)
DX_LASTCONTACT_DEATH_MONTHS (mean (sd)) 58.05 (40.30)
LYMPH_VASCULAR_INVASION_F (%) Neg_LymphVasc_Inv 271 ( 19.9)
Pos_LumphVasc_Inv 28 ( 2.1)
N_A 1 ( 0.1)
Unknown 438 ( 32.2)
NA 622 ( 45.7)
RX_HOSP_SURG_APPR_2010_F (%) No_Surg 122 ( 9.0)
Robot_Assist 2 ( 0.1)
Robot_to_Open 0 ( 0.0)
Endo_Lap 18 ( 1.3)
Endo_Lap_to_Open 2 ( 0.1)
Open_Unknown 594 ( 43.7)
Unknown 0 ( 0.0)
NA 622 ( 45.7)
SURG_RAD_SEQ (%) Surg Alone 1158 ( 85.1)
Surg then Rad 23 ( 1.7)
Rad Alone 52 ( 3.8)
No Treatment 105 ( 7.7)
Other 22 ( 1.6)
Rad before and after Surg 0 ( 0.0)
Rad then Surg 0 ( 0.0)
SURG_RAD_SEQ_C (%) Surg, No rad, No Chemo 1119 ( 82.3)
Surg then Rad, No Chemo 20 ( 1.5)
Surg then Rad, Yes Chemo 3 ( 0.2)
Surg, No rad, Yes Chemo 10 ( 0.7)
No Surg, No Rad, Yes Chemo 9 ( 0.7)
No Surg, No Rad, No Chemo 94 ( 6.9)
Other 53 ( 3.9)
Rad, No Surg, Yes Chemo 9 ( 0.7)
Rad, No Surg, No Chemo 43 ( 3.2)
Rad then Surg, Yes Chemo 0 ( 0.0)
Rad then Surg, No Chemo 0 ( 0.0)
Rad before and after Surg, Yes Chemo 0 ( 0.0)
Rad before and after Surg, No Chemo 0 ( 0.0)
SURGERY_YN (%) No 156 ( 11.5)
Ukn 8 ( 0.6)
Yes 1196 ( 87.9)
RADIATION_YN (%) No 1268 ( 93.2)
Yes 75 ( 5.5)
NA 17 ( 1.2)
CHEMO_YN (%) No 1287 ( 94.6)
Yes 31 ( 2.3)
Ukn 42 ( 3.1)
IMMUNO_YN (%) No 1279 ( 94.0)
Yes 71 ( 5.2)
Ukn 10 ( 0.7)
Tx_YN (%) FALSE 56 ( 4.1)
TRUE 1262 ( 92.8)
NA 42 ( 3.1)
mets_at_dx (%) Bone 5 ( 0.4)
Brain 0 ( 0.0)
Liver 0 ( 0.0)
Lung 0 ( 0.0)
None/Other/Unk/NA 1355 ( 99.6)
MEDICAID_EXPN_CODE (%) Non-Expansion State 482 ( 35.4)
Jan 2014 Expansion States 450 ( 33.1)
Early Expansion States (2010-13) 239 ( 17.6)
Late Expansion States (> Jan 2014) 175 ( 12.9)
Suppressed for Ages 0 - 39 14 ( 1.0)
EXPN_GROUP (%) Exclude 14 ( 1.0)
Post-Expansion 247 ( 18.2)
Pre-Expansion 1099 ( 80.8)
p_table(data,
        vars = c("FACILITY_TYPE_F", "FACILITY_LOCATION_F", "FACILITY_GEOGRAPHY",  "AGE", "AGE_F", "AGE_40",
                 "SEX_F", "RACE_F", "HISPANIC", "INSURANCE_F", 
                 "INCOME_F", "EDUCATION_F", "U_R_F", "CROWFLY", "CDCC_TOTAL_BEST",
                 "SITE_TEXT", "BEHAVIOR", "GRADE_F",
                 "DX_STAGING_PROC_DAYS", "TNM_CLIN_T", "TNM_CLIN_N", "TNM_CLIN_M",
                 "TNM_CLIN_STAGE_GROUP", "TNM_PATH_T", "TNM_PATH_N", "TNM_PATH_M",
                 "TNM_PATH_STAGE_GROUP", "DX_RX_STARTED_DAYS", "DX_SURG_STARTED_DAYS",
                 "DX_DEFSURG_STARTED_DAYS", "MARGINS", "MARGINS_YN", "SURG_DISCHARGE_DAYS",
                 "READM_HOSP_30_DAYS_F", "RX_SUMM_RADIATION_F", "PUF_30_DAY_MORT_CD_F",
                 "PUF_90_DAY_MORT_CD_F", "DX_LASTCONTACT_DEATH_MONTHS", 
                 "LYMPH_VASCULAR_INVASION_F", "RX_HOSP_SURG_APPR_2010_F", "SURG_RAD_SEQ",
                 "SURG_RAD_SEQ_C", "T_SIZE", "SURGERY_YN", "RADIATION_YN", "CHEMO_YN",
                 "IMMUNO_YN", "Tx_YN", "mets_at_dx",
                 "MEDICAID_EXPN_CODE"), 
        strata = "SURGERY_YN")
no non-missing arguments to min; returning Infno non-missing arguments to min; returning Infno non-missing arguments to min; returning Infno non-missing arguments to max; returning -Infno non-missing arguments to max; returning -Infno non-missing arguments to max; returning -Infno non-missing arguments to min; returning Infno non-missing arguments to min; returning Infno non-missing arguments to min; returning Infno non-missing arguments to max; returning -Infno non-missing arguments to max; returning -Infno non-missing arguments to max; returning -InfVariable has only NA's in at least one stratum. na.rm turned off.Variable has only NA's in at least one stratum. na.rm turned off.Variable has only NA's in at least one stratum. na.rm turned off.

level No Ukn Yes p test
n 156 8 1196
FACILITY_TYPE_F (%) Community Cancer Program 11 ( 7.1) 1 ( 12.5) 19 ( 1.6) <0.001
Comprehensive Comm Ca Program 64 ( 41.0) 4 ( 50.0) 394 ( 32.9)
Academic/Research Program 65 ( 41.7) 3 ( 37.5) 586 ( 49.0)
Integrated Network Ca Program 16 ( 10.3) 0 ( 0.0) 183 ( 15.3)
NA 0 ( 0.0) 0 ( 0.0) 14 ( 1.2)
FACILITY_LOCATION_F (%) New England 11 ( 7.1) 2 ( 25.0) 46 ( 3.8) 0.034
Middle Atlantic 25 ( 16.0) 3 ( 37.5) 203 ( 17.0)
South Atlantic 20 ( 12.8) 0 ( 0.0) 235 ( 19.6)
East North Central 23 ( 14.7) 2 ( 25.0) 197 ( 16.5)
East South Central 10 ( 6.4) 0 ( 0.0) 79 ( 6.6)
West North Central 20 ( 12.8) 0 ( 0.0) 121 ( 10.1)
West South Central 8 ( 5.1) 1 ( 12.5) 90 ( 7.5)
Mountain 11 ( 7.1) 0 ( 0.0) 68 ( 5.7)
Pacific 28 ( 17.9) 0 ( 0.0) 143 ( 12.0)
NA 0 ( 0.0) 0 ( 0.0) 14 ( 1.2)
FACILITY_GEOGRAPHY (%) Northeast 36 ( 23.1) 5 ( 62.5) 249 ( 20.8) 0.013
South 28 ( 17.9) 1 ( 12.5) 325 ( 27.2)
Midwest 53 ( 34.0) 2 ( 25.0) 397 ( 33.2)
West 39 ( 25.0) 0 ( 0.0) 211 ( 17.6)
NA 0 ( 0.0) 0 ( 0.0) 14 ( 1.2)
AGE (mean (sd)) 75.99 (11.78) 78.75 (10.05) 69.75 (11.69) <0.001
AGE_F (%) (0,54] 9 ( 5.8) 0 ( 0.0) 135 ( 11.3) <0.001
(54,64] 18 ( 11.5) 1 ( 12.5) 235 ( 19.6)
(64,74] 39 ( 25.0) 2 ( 25.0) 380 ( 31.8)
(74,100] 90 ( 57.7) 5 ( 62.5) 446 ( 37.3)
AGE_40 (%) (0,40] 0 ( 0.0) 0 ( 0.0) 18 ( 1.5) 0.286
(40,100] 156 (100.0) 8 (100.0) 1178 ( 98.5)
SEX_F (%) Male 53 ( 34.0) 2 ( 25.0) 206 ( 17.2) <0.001
Female 103 ( 66.0) 6 ( 75.0) 990 ( 82.8)
RACE_F (%) White 139 ( 89.1) 6 ( 75.0) 1084 ( 90.6) 0.296
Black 4 ( 2.6) 0 ( 0.0) 21 ( 1.8)
Other/Unk 4 ( 2.6) 0 ( 0.0) 31 ( 2.6)
Asian 9 ( 5.8) 2 ( 25.0) 60 ( 5.0)
HISPANIC (%) No 145 ( 92.9) 7 ( 87.5) 1082 ( 90.5) 0.608
Yes 6 ( 3.8) 0 ( 0.0) 46 ( 3.8)
Unknown 5 ( 3.2) 1 ( 12.5) 68 ( 5.7)
INSURANCE_F (%) Private 40 ( 25.6) 1 ( 12.5) 412 ( 34.4) 0.075
None 1 ( 0.6) 0 ( 0.0) 22 ( 1.8)
Medicaid 6 ( 3.8) 0 ( 0.0) 16 ( 1.3)
Medicare 103 ( 66.0) 7 ( 87.5) 728 ( 60.9)
Other Government 2 ( 1.3) 0 ( 0.0) 6 ( 0.5)
Unknown 4 ( 2.6) 0 ( 0.0) 12 ( 1.0)
INCOME_F (%) Less than $38,000 24 ( 15.4) 0 ( 0.0) 141 ( 11.8) 0.514
$38,000 - $47,999 29 ( 18.6) 2 ( 25.0) 295 ( 24.7)
$48,000 - $62,999 42 ( 26.9) 1 ( 12.5) 322 ( 26.9)
$63,000 + 60 ( 38.5) 5 ( 62.5) 434 ( 36.3)
NA 1 ( 0.6) 0 ( 0.0) 4 ( 0.3)
EDUCATION_F (%) 21% or more 23 ( 14.7) 0 ( 0.0) 144 ( 12.0) 0.351
13 - 20.9% 30 ( 19.2) 1 ( 12.5) 289 ( 24.2)
7 - 12.9% 64 ( 41.0) 4 ( 50.0) 397 ( 33.2)
Less than 7% 38 ( 24.4) 3 ( 37.5) 362 ( 30.3)
NA 1 ( 0.6) 0 ( 0.0) 4 ( 0.3)
U_R_F (%) Metro 126 ( 80.8) 6 ( 75.0) 974 ( 81.4) 0.799
Urban 22 ( 14.1) 1 ( 12.5) 164 ( 13.7)
Rural 4 ( 2.6) 0 ( 0.0) 23 ( 1.9)
NA 4 ( 2.6) 1 ( 12.5) 35 ( 2.9)
CROWFLY (mean (sd)) 19.10 (25.19) 104.33 (279.09) 43.59 (130.25) 0.025
CDCC_TOTAL_BEST (%) 0 126 ( 80.8) 7 ( 87.5) 973 ( 81.4) 0.621
1 22 ( 14.1) 0 ( 0.0) 176 ( 14.7)
2 6 ( 3.8) 1 ( 12.5) 33 ( 2.8)
3 2 ( 1.3) 0 ( 0.0) 14 ( 1.2)
SITE_TEXT (%) C00.0 External Lip: Upper NOS 0 ( 0.0) 0 ( 0.0) 0 ( 0.0) NaN
C00.1 External Lip: Lower NOS 0 ( 0.0) 0 ( 0.0) 0 ( 0.0)
C00.2 External Lip: NOS 0 ( 0.0) 0 ( 0.0) 0 ( 0.0)
C00.3 Lip: Upper Mucosa 0 ( 0.0) 0 ( 0.0) 0 ( 0.0)
C00.4 Lip: Lower Mucosa 0 ( 0.0) 0 ( 0.0) 0 ( 0.0)
C00.5 Lip: Mucosa NOS 0 ( 0.0) 0 ( 0.0) 0 ( 0.0)
C00.6 Lip: Commissure 0 ( 0.0) 0 ( 0.0) 0 ( 0.0)
C00.8 Lip: Overlapping 0 ( 0.0) 0 ( 0.0) 0 ( 0.0)
C00.9 Lip NOS 0 ( 0.0) 0 ( 0.0) 0 ( 0.0)
C01.9 Tongue: Base NOS 0 ( 0.0) 0 ( 0.0) 0 ( 0.0)
C02.0 Tongue: Dorsal NOS 0 ( 0.0) 0 ( 0.0) 0 ( 0.0)
C02.1 Tongue: Border, Tip 0 ( 0.0) 0 ( 0.0) 0 ( 0.0)
C02.2 Tongue: Ventral NOS 0 ( 0.0) 0 ( 0.0) 0 ( 0.0)
C02.3 Tongue: Anterior NOS 0 ( 0.0) 0 ( 0.0) 0 ( 0.0)
C02.4 Lingual Tonsil 0 ( 0.0) 0 ( 0.0) 0 ( 0.0)
C02.8 Tongue: Overlapping 0 ( 0.0) 0 ( 0.0) 0 ( 0.0)
C02.9 Tongue: NOS 0 ( 0.0) 0 ( 0.0) 0 ( 0.0)
C03.0 Gum: Upper 0 ( 0.0) 0 ( 0.0) 0 ( 0.0)
C03.1 Gum: Lower 0 ( 0.0) 0 ( 0.0) 0 ( 0.0)
C03.9 Gum NOS 0 ( 0.0) 0 ( 0.0) 0 ( 0.0)
C04.0 Mouth: Anterior Floor 0 ( 0.0) 0 ( 0.0) 0 ( 0.0)
C04.1 Mouth: Lateral Floor 0 ( 0.0) 0 ( 0.0) 0 ( 0.0)
C04.9 Floor of Mouth NOS 0 ( 0.0) 0 ( 0.0) 0 ( 0.0)
C05.0 Hard Palate 0 ( 0.0) 0 ( 0.0) 0 ( 0.0)
C05.1 Soft Palate NOS 0 ( 0.0) 0 ( 0.0) 0 ( 0.0)
C05.2 Uvula 0 ( 0.0) 0 ( 0.0) 0 ( 0.0)
C05.8 Palate: Overlapping 0 ( 0.0) 0 ( 0.0) 0 ( 0.0)
C05.9 Palate NOS 0 ( 0.0) 0 ( 0.0) 0 ( 0.0)
C06.0 Cheek Mucosa 0 ( 0.0) 0 ( 0.0) 0 ( 0.0)
C06.1 Mouth: Vestibule 0 ( 0.0) 0 ( 0.0) 0 ( 0.0)
C06.2 Retromolar Area 0 ( 0.0) 0 ( 0.0) 0 ( 0.0)
C06.8 Mouth: Other Overlapping 0 ( 0.0) 0 ( 0.0) 0 ( 0.0)
C06.9 Mouth NOS 0 ( 0.0) 0 ( 0.0) 0 ( 0.0)
C07.9 Parotid Gland 0 ( 0.0) 0 ( 0.0) 0 ( 0.0)
C09.8 Tonsil: Overlapping 0 ( 0.0) 0 ( 0.0) 0 ( 0.0)
C09.9 Tonsil NOS 0 ( 0.0) 0 ( 0.0) 0 ( 0.0)
C11.1 Nasopharynx: Poster Wall 0 ( 0.0) 0 ( 0.0) 0 ( 0.0)
C14.2 Waldeyer Ring 0 ( 0.0) 0 ( 0.0) 0 ( 0.0)
C30.0 Nasal Cavity 0 ( 0.0) 0 ( 0.0) 0 ( 0.0)
C37.9 Thymus 0 ( 0.0) 0 ( 0.0) 0 ( 0.0)
C42.0 Blood 0 ( 0.0) 0 ( 0.0) 0 ( 0.0)
C42.2 Spleen 0 ( 0.0) 0 ( 0.0) 0 ( 0.0)
C42.4 Hematopoietic NOS 0 ( 0.0) 0 ( 0.0) 0 ( 0.0)
C44.0 Skin of lip, NOS 0 ( 0.0) 0 ( 0.0) 0 ( 0.0)
C44.1 Eyelid 0 ( 0.0) 0 ( 0.0) 0 ( 0.0)
C44.2 External ear 0 ( 0.0) 0 ( 0.0) 2 ( 0.2)
C44.3 Skin of ear and unspecified parts of face 0 ( 0.0) 0 ( 0.0) 2 ( 0.2)
C44.4 Skin of scalp and neck 0 ( 0.0) 0 ( 0.0) 3 ( 0.3)
C44.5 Skin of trunk 64 ( 41.0) 1 ( 12.5) 222 ( 18.6)
C44.6 Skin of upper limb and shoulder 2 ( 1.3) 0 ( 0.0) 5 ( 0.4)
C44.7 Skin of lower limb and hip 2 ( 1.3) 1 ( 12.5) 9 ( 0.8)
C44.8 Overlapping lesion of skin 3 ( 1.9) 0 ( 0.0) 8 ( 0.7)
C44.9 Skin, NOS 5 ( 3.2) 0 ( 0.0) 14 ( 1.2)
C50.0 Nipple 0 ( 0.0) 0 ( 0.0) 0 ( 0.0)
C51.0 Labium majus 8 ( 5.1) 1 ( 12.5) 83 ( 6.9)
C51.1 Labium minus 2 ( 1.3) 0 ( 0.0) 13 ( 1.1)
C51.2 Clitoris 0 ( 0.0) 0 ( 0.0) 2 ( 0.2)
C51.8 Overlapping lesion of vulva 3 ( 1.9) 0 ( 0.0) 57 ( 4.8)
C51.9 Vulva, NOS 65 ( 41.7) 5 ( 62.5) 752 ( 62.9)
C52.9 Vagina, NOS 0 ( 0.0) 0 ( 0.0) 1 ( 0.1)
C60.0 Prepuce 0 ( 0.0) 0 ( 0.0) 0 ( 0.0)
C60.1 Glans penis 0 ( 0.0) 0 ( 0.0) 0 ( 0.0)
C60.2 Body of penis 0 ( 0.0) 0 ( 0.0) 1 ( 0.1)
C60.8 Overlapping lesion of penis 0 ( 0.0) 0 ( 0.0) 2 ( 0.2)
C60.9 Penis 2 ( 1.3) 0 ( 0.0) 20 ( 1.7)
C63.2 Scrotum, NOS 0 ( 0.0) 0 ( 0.0) 0 ( 0.0)
C77.0 Lymph Nodes: HeadFaceNeck 0 ( 0.0) 0 ( 0.0) 0 ( 0.0)
C77.1 Intrathoracic Lymph Nodes 0 ( 0.0) 0 ( 0.0) 0 ( 0.0)
C77.2 Intra-abdominal LymphNodes 0 ( 0.0) 0 ( 0.0) 0 ( 0.0)
C77.3 Lymph Nodes of axilla or arm 0 ( 0.0) 0 ( 0.0) 0 ( 0.0)
C77.4 Lymph Nodes: Leg 0 ( 0.0) 0 ( 0.0) 0 ( 0.0)
C77.5 Pelvic Lymph Nodes 0 ( 0.0) 0 ( 0.0) 0 ( 0.0)
C77.8 Lymph Nodes: multiple region 0 ( 0.0) 0 ( 0.0) 0 ( 0.0)
C77.9 Lymph Node NOS 0 ( 0.0) 0 ( 0.0) 0 ( 0.0)
BEHAVIOR (%) 2 0 ( 0.0) 0 ( 0.0) 0 ( 0.0) NaN
3 156 (100.0) 8 (100.0) 1196 (100.0)
GRADE_F (%) Gr I: Well Diff 0 ( 0.0) 0 ( 0.0) 38 ( 3.2) NaN
Gr II: Mod Diff 2 ( 1.3) 0 ( 0.0) 28 ( 2.3)
Gr III: Poor Diff 3 ( 1.9) 0 ( 0.0) 33 ( 2.8)
Gr IV: Undiff/Anaplastic 0 ( 0.0) 0 ( 0.0) 0 ( 0.0)
5 0 ( 0.0) 0 ( 0.0) 0 ( 0.0)
6 0 ( 0.0) 0 ( 0.0) 0 ( 0.0)
7 0 ( 0.0) 0 ( 0.0) 0 ( 0.0)
8 0 ( 0.0) 0 ( 0.0) 0 ( 0.0)
NA/Unkown 151 ( 96.8) 8 (100.0) 1097 ( 91.7)
DX_STAGING_PROC_DAYS (mean (sd)) 4.40 (25.98) 0.00 (0.00) 1.51 (10.75) 0.071
TNM_CLIN_T (%) N_A 0 ( 0.0) 1 ( 12.5) 0 ( 0.0) NaN
c0 0 ( 0.0) 0 ( 0.0) 6 ( 0.5)
c1 11 ( 7.1) 0 ( 0.0) 128 ( 10.7)
c1A 10 ( 6.4) 1 ( 12.5) 137 ( 11.5)
c1B 11 ( 7.1) 0 ( 0.0) 127 ( 10.6)
c1C 0 ( 0.0) 0 ( 0.0) 0 ( 0.0)
c1MI 0 ( 0.0) 0 ( 0.0) 0 ( 0.0)
c2 25 ( 16.0) 0 ( 0.0) 151 ( 12.6)
c2A 0 ( 0.0) 0 ( 0.0) 0 ( 0.0)
c2B 0 ( 0.0) 0 ( 0.0) 0 ( 0.0)
c2C 0 ( 0.0) 0 ( 0.0) 0 ( 0.0)
c2D 0 ( 0.0) 0 ( 0.0) 0 ( 0.0)
c3 7 ( 4.5) 0 ( 0.0) 18 ( 1.5)
c3A 0 ( 0.0) 0 ( 0.0) 0 ( 0.0)
c3B 0 ( 0.0) 0 ( 0.0) 0 ( 0.0)
c4 1 ( 0.6) 0 ( 0.0) 0 ( 0.0)
c4A 0 ( 0.0) 0 ( 0.0) 0 ( 0.0)
c4B 0 ( 0.0) 0 ( 0.0) 0 ( 0.0)
c4C 0 ( 0.0) 0 ( 0.0) 0 ( 0.0)
c4D 0 ( 0.0) 0 ( 0.0) 0 ( 0.0)
cX 82 ( 52.6) 6 ( 75.0) 537 ( 44.9)
pA 0 ( 0.0) 0 ( 0.0) 0 ( 0.0)
pIS 0 ( 0.0) 0 ( 0.0) 28 ( 2.3)
NA 9 ( 5.8) 0 ( 0.0) 64 ( 5.4)
TNM_CLIN_N (%) N_A 0 ( 0.0) 1 ( 12.5) 0 ( 0.0) NaN
c0 91 ( 58.3) 2 ( 25.0) 779 ( 65.1)
c1 2 ( 1.3) 0 ( 0.0) 2 ( 0.2)
c1A 0 ( 0.0) 0 ( 0.0) 0 ( 0.0)
c1B 0 ( 0.0) 0 ( 0.0) 0 ( 0.0)
c2 1 ( 0.6) 1 ( 12.5) 2 ( 0.2)
c2A 0 ( 0.0) 0 ( 0.0) 0 ( 0.0)
c2B 1 ( 0.6) 0 ( 0.0) 2 ( 0.2)
c2C 0 ( 0.0) 0 ( 0.0) 1 ( 0.1)
c3 0 ( 0.0) 0 ( 0.0) 0 ( 0.0)
c3A 0 ( 0.0) 0 ( 0.0) 0 ( 0.0)
c3B 0 ( 0.0) 0 ( 0.0) 0 ( 0.0)
c3C 0 ( 0.0) 0 ( 0.0) 0 ( 0.0)
c4 0 ( 0.0) 0 ( 0.0) 0 ( 0.0)
cX 53 ( 34.0) 4 ( 50.0) 355 ( 29.7)
NA 8 ( 5.1) 0 ( 0.0) 55 ( 4.6)
TNM_CLIN_M (%) N_A 0 ( 0.0) 1 ( 12.5) 0 ( 0.0) NaN
c0 138 ( 88.5) 6 ( 75.0) 1104 ( 92.3)
c0I+ 0 ( 0.0) 0 ( 0.0) 0 ( 0.0)
c1 6 ( 3.8) 1 ( 12.5) 4 ( 0.3)
c1A 0 ( 0.0) 0 ( 0.0) 0 ( 0.0)
c1B 0 ( 0.0) 0 ( 0.0) 0 ( 0.0)
c1C 0 ( 0.0) 0 ( 0.0) 0 ( 0.0)
NA 12 ( 7.7) 0 ( 0.0) 88 ( 7.4)
TNM_CLIN_STAGE_GROUP (%) 0 2 ( 1.3) 0 ( 0.0) 62 ( 5.2) NaN
1 15 ( 9.6) 1 ( 12.5) 180 ( 15.1)
1A 5 ( 3.2) 0 ( 0.0) 104 ( 8.7)
1B 10 ( 6.4) 0 ( 0.0) 109 ( 9.1)
1C 0 ( 0.0) 0 ( 0.0) 0 ( 0.0)
2 26 ( 16.7) 0 ( 0.0) 158 ( 13.2)
2A 0 ( 0.0) 0 ( 0.0) 0 ( 0.0)
2B 0 ( 0.0) 0 ( 0.0) 0 ( 0.0)
2C 0 ( 0.0) 0 ( 0.0) 0 ( 0.0)
3 2 ( 1.3) 0 ( 0.0) 13 ( 1.1)
3A 0 ( 0.0) 0 ( 0.0) 0 ( 0.0)
3B 0 ( 0.0) 0 ( 0.0) 1 ( 0.1)
3C 0 ( 0.0) 0 ( 0.0) 0 ( 0.0)
4 7 ( 4.5) 0 ( 0.0) 2 ( 0.2)
4A 0 ( 0.0) 0 ( 0.0) 2 ( 0.2)
4A1 0 ( 0.0) 0 ( 0.0) 0 ( 0.0)
4A2 0 ( 0.0) 0 ( 0.0) 0 ( 0.0)
4B 1 ( 0.6) 1 ( 12.5) 3 ( 0.3)
4C 0 ( 0.0) 0 ( 0.0) 0 ( 0.0)
N_A 0 ( 0.0) 1 ( 12.5) 0 ( 0.0)
99 88 ( 56.4) 5 ( 62.5) 562 ( 47.0)
TNM_PATH_T (%) N_A 0 ( 0.0) 1 ( 12.5) 0 ( 0.0) NaN
p0 0 ( 0.0) 0 ( 0.0) 9 ( 0.8)
p1 1 ( 0.6) 0 ( 0.0) 97 ( 8.1)
p1A 0 ( 0.0) 0 ( 0.0) 147 ( 12.3)
p1B 0 ( 0.0) 0 ( 0.0) 157 ( 13.1)
p1C 0 ( 0.0) 0 ( 0.0) 0 ( 0.0)
p1MI 0 ( 0.0) 0 ( 0.0) 0 ( 0.0)
p2 1 ( 0.6) 0 ( 0.0) 143 ( 12.0)
p2A 0 ( 0.0) 0 ( 0.0) 0 ( 0.0)
p2B 0 ( 0.0) 0 ( 0.0) 0 ( 0.0)
p2C 0 ( 0.0) 0 ( 0.0) 0 ( 0.0)
p2D 0 ( 0.0) 0 ( 0.0) 0 ( 0.0)
p3 0 ( 0.0) 0 ( 0.0) 22 ( 1.8)
p3A 0 ( 0.0) 0 ( 0.0) 0 ( 0.0)
p3B 0 ( 0.0) 0 ( 0.0) 0 ( 0.0)
p4 0 ( 0.0) 0 ( 0.0) 1 ( 0.1)
p4A 0 ( 0.0) 0 ( 0.0) 0 ( 0.0)
p4B 0 ( 0.0) 0 ( 0.0) 0 ( 0.0)
p4C 0 ( 0.0) 0 ( 0.0) 0 ( 0.0)
p4D 0 ( 0.0) 0 ( 0.0) 0 ( 0.0)
pA 0 ( 0.0) 0 ( 0.0) 0 ( 0.0)
pIS 0 ( 0.0) 0 ( 0.0) 23 ( 1.9)
pX 97 ( 62.2) 7 ( 87.5) 497 ( 41.6)
NA 57 ( 36.5) 0 ( 0.0) 100 ( 8.4)
TNM_PATH_N (%) N_A 0 ( 0.0) 1 ( 12.5) 0 ( 0.0) NaN
p0 1 ( 0.6) 0 ( 0.0) 281 ( 23.5)
p0I- 0 ( 0.0) 0 ( 0.0) 0 ( 0.0)
p0I+ 0 ( 0.0) 0 ( 0.0) 0 ( 0.0)
p0M- 0 ( 0.0) 0 ( 0.0) 0 ( 0.0)
p0M+ 0 ( 0.0) 0 ( 0.0) 0 ( 0.0)
p1 1 ( 0.6) 0 ( 0.0) 8 ( 0.7)
p1A 0 ( 0.0) 0 ( 0.0) 1 ( 0.1)
p1B 0 ( 0.0) 0 ( 0.0) 1 ( 0.1)
p1C 0 ( 0.0) 0 ( 0.0) 0 ( 0.0)
p1MI 0 ( 0.0) 0 ( 0.0) 0 ( 0.0)
p2 0 ( 0.0) 0 ( 0.0) 1 ( 0.1)
p2A 0 ( 0.0) 0 ( 0.0) 0 ( 0.0)
p2B 0 ( 0.0) 0 ( 0.0) 1 ( 0.1)
p2C 0 ( 0.0) 0 ( 0.0) 6 ( 0.5)
p3 0 ( 0.0) 0 ( 0.0) 1 ( 0.1)
p3A 0 ( 0.0) 0 ( 0.0) 0 ( 0.0)
p3B 0 ( 0.0) 0 ( 0.0) 0 ( 0.0)
p3C 0 ( 0.0) 0 ( 0.0) 0 ( 0.0)
p4 0 ( 0.0) 0 ( 0.0) 0 ( 0.0)
pX 97 ( 62.2) 7 ( 87.5) 740 ( 61.9)
NA 57 ( 36.5) 0 ( 0.0) 156 ( 13.0)
TNM_PATH_M (%) N_A 0 ( 0.0) 1 ( 12.5) 0 ( 0.0) NaN
p0 0 ( 0.0) 0 ( 0.0) 0 ( 0.0)
p1 1 ( 0.6) 0 ( 0.0) 2 ( 0.2)
p1A 0 ( 0.0) 0 ( 0.0) 0 ( 0.0)
p1B 0 ( 0.0) 0 ( 0.0) 0 ( 0.0)
p1C 0 ( 0.0) 0 ( 0.0) 0 ( 0.0)
pX 57 ( 36.5) 6 ( 75.0) 559 ( 46.7)
NA 98 ( 62.8) 1 ( 12.5) 635 ( 53.1)
TNM_PATH_STAGE_GROUP (%) 0 1 ( 0.6) 0 ( 0.0) 57 ( 4.8) NaN
1 0 ( 0.0) 0 ( 0.0) 107 ( 8.9)
1A 0 ( 0.0) 0 ( 0.0) 104 ( 8.7)
1B 0 ( 0.0) 0 ( 0.0) 92 ( 7.7)
1C 0 ( 0.0) 0 ( 0.0) 0 ( 0.0)
2 1 ( 0.6) 0 ( 0.0) 119 ( 9.9)
2A 0 ( 0.0) 0 ( 0.0) 0 ( 0.0)
2B 0 ( 0.0) 0 ( 0.0) 0 ( 0.0)
2C 0 ( 0.0) 0 ( 0.0) 0 ( 0.0)
3 0 ( 0.0) 0 ( 0.0) 26 ( 2.2)
3A 0 ( 0.0) 0 ( 0.0) 2 ( 0.2)
3B 0 ( 0.0) 0 ( 0.0) 0 ( 0.0)
3C 0 ( 0.0) 0 ( 0.0) 5 ( 0.4)
4 2 ( 1.3) 0 ( 0.0) 2 ( 0.2)
4A 0 ( 0.0) 0 ( 0.0) 5 ( 0.4)
4A1 0 ( 0.0) 0 ( 0.0) 0 ( 0.0)
4B 0 ( 0.0) 0 ( 0.0) 1 ( 0.1)
4C 0 ( 0.0) 0 ( 0.0) 0 ( 0.0)
N_A 0 ( 0.0) 1 ( 12.5) 0 ( 0.0)
99 119 ( 76.3) 7 ( 87.5) 630 ( 52.7)
NA 33 ( 21.2) 0 ( 0.0) 46 ( 3.8)
DX_RX_STARTED_DAYS (mean (sd)) 95.13 (249.98) 59.00 (NA) 45.52 (118.50) NA
DX_SURG_STARTED_DAYS (mean (sd)) NaN (NA) NaN (NA) 46.72 (118.95) NA
DX_DEFSURG_STARTED_DAYS (mean (sd)) NaN (NA) NaN (NA) 58.37 (134.05) NA
MARGINS (%) No Residual 0 ( 0.0) 0 ( 0.0) 601 ( 50.3) <0.001
Residual, NOS 0 ( 0.0) 0 ( 0.0) 164 ( 13.7)
Microscopic Resid 0 ( 0.0) 0 ( 0.0) 351 ( 29.3)
Macroscopic Resid 0 ( 0.0) 0 ( 0.0) 17 ( 1.4)
Not evaluable 0 ( 0.0) 0 ( 0.0) 22 ( 1.8)
No surg 156 (100.0) 5 ( 62.5) 0 ( 0.0)
Unknown 0 ( 0.0) 3 ( 37.5) 41 ( 3.4)
MARGINS_YN (%) No 0 ( 0.0) 0 ( 0.0) 601 ( 50.3) <0.001
Yes 0 ( 0.0) 0 ( 0.0) 532 ( 44.5)
No surg/Unk/NA 156 (100.0) 8 (100.0) 63 ( 5.3)
SURG_DISCHARGE_DAYS (mean (sd)) NaN (NA) NaN (NA) 2.12 (6.93) NA
READM_HOSP_30_DAYS_F (%) No_Surg_or_No_Readmit 153 ( 98.1) 3 ( 37.5) 1136 ( 95.0) <0.001
Unplan_Readmit_Same 2 ( 1.3) 0 ( 0.0) 34 ( 2.8)
Plan_Readmit_Same 0 ( 0.0) 0 ( 0.0) 15 ( 1.3)
PlanUnplan_Same 0 ( 0.0) 0 ( 0.0) 1 ( 0.1)
9 1 ( 0.6) 5 ( 62.5) 10 ( 0.8)
RX_SUMM_RADIATION_F (%) None 105 ( 67.3) 5 ( 62.5) 1158 ( 96.8) NaN
Beam Radiation 49 ( 31.4) 1 ( 12.5) 23 ( 1.9)
Radioactive Implants 1 ( 0.6) 0 ( 0.0) 0 ( 0.0)
Radioisotopes 0 ( 0.0) 0 ( 0.0) 0 ( 0.0)
Beam + Imp or Isotopes 1 ( 0.6) 0 ( 0.0) 0 ( 0.0)
Radiation, NOS 0 ( 0.0) 0 ( 0.0) 0 ( 0.0)
Unknown 0 ( 0.0) 2 ( 25.0) 15 ( 1.3)
PUF_30_DAY_MORT_CD_F (%) Alive_30 0 ( 0.0) 0 ( 0.0) 1156 ( 96.7) <0.001
Dead_30 0 ( 0.0) 0 ( 0.0) 5 ( 0.4)
Unknown 0 ( 0.0) 0 ( 0.0) 33 ( 2.8)
NA 156 (100.0) 8 (100.0) 2 ( 0.2)
PUF_90_DAY_MORT_CD_F (%) Alive_90 0 ( 0.0) 0 ( 0.0) 1134 ( 94.8) <0.001
Dead_90 0 ( 0.0) 0 ( 0.0) 10 ( 0.8)
Unknown 0 ( 0.0) 0 ( 0.0) 50 ( 4.2)
NA 156 (100.0) 8 (100.0) 2 ( 0.2)
DX_LASTCONTACT_DEATH_MONTHS (mean (sd)) 38.15 (31.36) 41.41 (34.67) 60.75 (40.64) <0.001
LYMPH_VASCULAR_INVASION_F (%) Neg_LymphVasc_Inv 15 ( 9.6) 0 ( 0.0) 256 ( 21.4) <0.001
Pos_LumphVasc_Inv 1 ( 0.6) 0 ( 0.0) 27 ( 2.3)
N_A 0 ( 0.0) 0 ( 0.0) 1 ( 0.1)
Unknown 83 ( 53.2) 2 ( 25.0) 353 ( 29.5)
NA 57 ( 36.5) 6 ( 75.0) 559 ( 46.7)
RX_HOSP_SURG_APPR_2010_F (%) No_Surg 99 ( 63.5) 2 ( 25.0) 21 ( 1.8) NaN
Robot_Assist 0 ( 0.0) 0 ( 0.0) 2 ( 0.2)
Robot_to_Open 0 ( 0.0) 0 ( 0.0) 0 ( 0.0)
Endo_Lap 0 ( 0.0) 0 ( 0.0) 18 ( 1.5)
Endo_Lap_to_Open 0 ( 0.0) 0 ( 0.0) 2 ( 0.2)
Open_Unknown 0 ( 0.0) 0 ( 0.0) 594 ( 49.7)
Unknown 0 ( 0.0) 0 ( 0.0) 0 ( 0.0)
NA 57 ( 36.5) 6 ( 75.0) 559 ( 46.7)
SURG_RAD_SEQ (%) Surg Alone 0 ( 0.0) 0 ( 0.0) 1158 ( 96.8) NaN
Surg then Rad 0 ( 0.0) 0 ( 0.0) 23 ( 1.9)
Rad Alone 51 ( 32.7) 1 ( 12.5) 0 ( 0.0)
No Treatment 105 ( 67.3) 0 ( 0.0) 0 ( 0.0)
Other 0 ( 0.0) 7 ( 87.5) 15 ( 1.3)
Rad before and after Surg 0 ( 0.0) 0 ( 0.0) 0 ( 0.0)
Rad then Surg 0 ( 0.0) 0 ( 0.0) 0 ( 0.0)
SURG_RAD_SEQ_C (%) Surg, No rad, No Chemo 0 ( 0.0) 0 ( 0.0) 1119 ( 93.6) NaN
Surg then Rad, No Chemo 0 ( 0.0) 0 ( 0.0) 20 ( 1.7)
Surg then Rad, Yes Chemo 0 ( 0.0) 0 ( 0.0) 3 ( 0.3)
Surg, No rad, Yes Chemo 0 ( 0.0) 0 ( 0.0) 10 ( 0.8)
No Surg, No Rad, Yes Chemo 9 ( 5.8) 0 ( 0.0) 0 ( 0.0)
No Surg, No Rad, No Chemo 94 ( 60.3) 0 ( 0.0) 0 ( 0.0)
Other 2 ( 1.3) 7 ( 87.5) 44 ( 3.7)
Rad, No Surg, Yes Chemo 8 ( 5.1) 1 ( 12.5) 0 ( 0.0)
Rad, No Surg, No Chemo 43 ( 27.6) 0 ( 0.0) 0 ( 0.0)
Rad then Surg, Yes Chemo 0 ( 0.0) 0 ( 0.0) 0 ( 0.0)
Rad then Surg, No Chemo 0 ( 0.0) 0 ( 0.0) 0 ( 0.0)
Rad before and after Surg, Yes Chemo 0 ( 0.0) 0 ( 0.0) 0 ( 0.0)
Rad before and after Surg, No Chemo 0 ( 0.0) 0 ( 0.0) 0 ( 0.0)
T_SIZE (%) No Tumor 0 ( 0.0) 0 ( 0.0) 1 ( 0.1) <0.001
Microscopic focus 1 ( 0.6) 1 ( 12.5) 23 ( 1.9)
< 1 cm 6 ( 3.8) 0 ( 0.0) 90 ( 7.5)
1-2 cm 3 ( 1.9) 0 ( 0.0) 94 ( 7.9)
2-3 cm 1 ( 0.6) 0 ( 0.0) 94 ( 7.9)
3-4 cm 8 ( 5.1) 0 ( 0.0) 109 ( 9.1)
4-5 cm 3 ( 1.9) 0 ( 0.0) 77 ( 6.4)
5-6 cm 9 ( 5.8) 0 ( 0.0) 63 ( 5.3)
>6 cm 22 ( 14.1) 1 ( 12.5) 152 ( 12.7)
NA_unk 103 ( 66.0) 6 ( 75.0) 493 ( 41.2)
SURGERY_YN (%) No 156 (100.0) 0 ( 0.0) 0 ( 0.0) <0.001
Ukn 0 ( 0.0) 8 (100.0) 0 ( 0.0)
Yes 0 ( 0.0) 0 ( 0.0) 1196 (100.0)
RADIATION_YN (%) No 105 ( 67.3) 5 ( 62.5) 1158 ( 96.8) <0.001
Yes 51 ( 32.7) 1 ( 12.5) 23 ( 1.9)
NA 0 ( 0.0) 2 ( 25.0) 15 ( 1.3)
CHEMO_YN (%) No 137 ( 87.8) 5 ( 62.5) 1145 ( 95.7) <0.001
Yes 17 ( 10.9) 1 ( 12.5) 13 ( 1.1)
Ukn 2 ( 1.3) 2 ( 25.0) 38 ( 3.2)
IMMUNO_YN (%) No 113 ( 72.4) 6 ( 75.0) 1160 ( 97.0) <0.001
Yes 43 ( 27.6) 0 ( 0.0) 28 ( 2.3)
Ukn 0 ( 0.0) 2 ( 25.0) 8 ( 0.7)
Tx_YN (%) FALSE 56 ( 35.9) 0 ( 0.0) 0 ( 0.0) <0.001
TRUE 98 ( 62.8) 6 ( 75.0) 1158 ( 96.8)
NA 2 ( 1.3) 2 ( 25.0) 38 ( 3.2)
mets_at_dx (%) Bone 3 ( 1.9) 0 ( 0.0) 2 ( 0.2) NaN
Brain 0 ( 0.0) 0 ( 0.0) 0 ( 0.0)
Liver 0 ( 0.0) 0 ( 0.0) 0 ( 0.0)
Lung 0 ( 0.0) 0 ( 0.0) 0 ( 0.0)
None/Other/Unk/NA 153 ( 98.1) 8 (100.0) 1194 ( 99.8)
MEDICAID_EXPN_CODE (%) Non-Expansion State 40 ( 25.6) 1 ( 12.5) 441 ( 36.9) 0.013
Jan 2014 Expansion States 67 ( 42.9) 3 ( 37.5) 380 ( 31.8)
Early Expansion States (2010-13) 33 ( 21.2) 1 ( 12.5) 205 ( 17.1)
Late Expansion States (> Jan 2014) 16 ( 10.3) 3 ( 37.5) 156 ( 13.0)
Suppressed for Ages 0 - 39 0 ( 0.0) 0 ( 0.0) 14 ( 1.2)

p_table(data,
        vars = c("FACILITY_TYPE_F", "FACILITY_LOCATION_F", "FACILITY_GEOGRAPHY",  "AGE", "AGE_F", "AGE_40",
                 "SEX_F", "RACE_F", "HISPANIC", "INSURANCE_F", 
                 "INCOME_F", "EDUCATION_F", "U_R_F", "CROWFLY", "CDCC_TOTAL_BEST",
                 "SITE_TEXT", "BEHAVIOR", "GRADE_F",
                 "DX_STAGING_PROC_DAYS", "TNM_CLIN_T", "TNM_CLIN_N", "TNM_CLIN_M",
                 "TNM_CLIN_STAGE_GROUP", "TNM_PATH_T", "TNM_PATH_N", "TNM_PATH_M",
                 "TNM_PATH_STAGE_GROUP", "DX_RX_STARTED_DAYS", "DX_SURG_STARTED_DAYS",
                 "DX_DEFSURG_STARTED_DAYS", "MARGINS", "MARGINS_YN", "SURG_DISCHARGE_DAYS",
                 "READM_HOSP_30_DAYS_F", "RX_SUMM_RADIATION_F", "PUF_30_DAY_MORT_CD_F",
                 "PUF_90_DAY_MORT_CD_F", "DX_LASTCONTACT_DEATH_MONTHS", 
                 "LYMPH_VASCULAR_INVASION_F", "RX_HOSP_SURG_APPR_2010_F", "SURG_RAD_SEQ",
                 "SURG_RAD_SEQ_C", "T_SIZE", "SURGERY_YN", "RADIATION_YN", 
                 "CHEMO_YN", "IMMUNO_YN", "Tx_YN", "mets_at_dx",
                 "MEDICAID_EXPN_CODE"), 
        strata = "RADIATION_YN")

level No Yes p test
n 1268 75
FACILITY_TYPE_F (%) Community Cancer Program 25 ( 2.0) 6 ( 8.0) 0.006
Comprehensive Comm Ca Program 421 ( 33.2) 30 ( 40.0)
Academic/Research Program 619 ( 48.8) 30 ( 40.0)
Integrated Network Ca Program 189 ( 14.9) 9 ( 12.0)
NA 14 ( 1.1) 0 ( 0.0)
FACILITY_LOCATION_F (%) New England 54 ( 4.3) 4 ( 5.3) 0.930
Middle Atlantic 218 ( 17.2) 9 ( 12.0)
South Atlantic 239 ( 18.8) 14 ( 18.7)
East North Central 208 ( 16.4) 12 ( 16.0)
East South Central 83 ( 6.5) 5 ( 6.7)
West North Central 131 ( 10.3) 9 ( 12.0)
West South Central 90 ( 7.1) 7 ( 9.3)
Mountain 69 ( 5.4) 6 ( 8.0)
Pacific 162 ( 12.8) 9 ( 12.0)
NA 14 ( 1.1) 0 ( 0.0)
FACILITY_GEOGRAPHY (%) Northeast 272 ( 21.5) 13 ( 17.3) 0.796
South 329 ( 25.9) 21 ( 28.0)
Midwest 422 ( 33.3) 26 ( 34.7)
West 231 ( 18.2) 15 ( 20.0)
NA 14 ( 1.1) 0 ( 0.0)
AGE (mean (sd)) 70.32 (11.86) 73.48 (11.94) 0.025
AGE_F (%) (0,54] 139 ( 11.0) 4 ( 5.3) 0.116
(54,64] 238 ( 18.8) 13 ( 17.3)
(64,74] 395 ( 31.2) 19 ( 25.3)
(74,100] 496 ( 39.1) 39 ( 52.0)
AGE_40 (%) (0,40] 18 ( 1.4) 0 ( 0.0) 0.602
(40,100] 1250 ( 98.6) 75 (100.0)
SEX_F (%) Male 226 ( 17.8) 33 ( 44.0) <0.001
Female 1042 ( 82.2) 42 ( 56.0)
RACE_F (%) White 1146 ( 90.4) 68 ( 90.7) 0.346
Black 24 ( 1.9) 1 ( 1.3)
Other/Unk 34 ( 2.7) 0 ( 0.0)
Asian 64 ( 5.0) 6 ( 8.0)
HISPANIC (%) No 1151 ( 90.8) 68 ( 90.7) 0.700
Yes 48 ( 3.8) 4 ( 5.3)
Unknown 69 ( 5.4) 3 ( 4.0)
INSURANCE_F (%) Private 424 ( 33.4) 22 ( 29.3) 0.704
None 22 ( 1.7) 1 ( 1.3)
Medicaid 21 ( 1.7) 1 ( 1.3)
Medicare 781 ( 61.6) 48 ( 64.0)
Other Government 7 ( 0.6) 1 ( 1.3)
Unknown 13 ( 1.0) 2 ( 2.7)
INCOME_F (%) Less than $38,000 152 ( 12.0) 11 ( 14.7) 0.916
$38,000 - $47,999 306 ( 24.1) 19 ( 25.3)
$48,000 - $62,999 342 ( 27.0) 20 ( 26.7)
$63,000 + 463 ( 36.5) 25 ( 33.3)
NA 5 ( 0.4) 0 ( 0.0)
EDUCATION_F (%) 21% or more 157 ( 12.4) 9 ( 12.0) 0.882
13 - 20.9% 301 ( 23.7) 18 ( 24.0)
7 - 12.9% 431 ( 34.0) 29 ( 38.7)
Less than 7% 374 ( 29.5) 19 ( 25.3)
NA 5 ( 0.4) 0 ( 0.0)
U_R_F (%) Metro 1032 ( 81.4) 57 ( 76.0) 0.573
Urban 173 ( 13.6) 14 ( 18.7)
Rural 26 ( 2.1) 1 ( 1.3)
NA 37 ( 2.9) 3 ( 4.0)
CROWFLY (mean (sd)) 42.45 (128.46) 24.64 (35.88) 0.231
CDCC_TOTAL_BEST (%) 0 1036 ( 81.7) 57 ( 76.0) 0.341
1 182 ( 14.4) 12 ( 16.0)
2 36 ( 2.8) 4 ( 5.3)
3 14 ( 1.1) 2 ( 2.7)
SITE_TEXT (%) C00.0 External Lip: Upper NOS 0 ( 0.0) 0 ( 0.0) NaN
C00.1 External Lip: Lower NOS 0 ( 0.0) 0 ( 0.0)
C00.2 External Lip: NOS 0 ( 0.0) 0 ( 0.0)
C00.3 Lip: Upper Mucosa 0 ( 0.0) 0 ( 0.0)
C00.4 Lip: Lower Mucosa 0 ( 0.0) 0 ( 0.0)
C00.5 Lip: Mucosa NOS 0 ( 0.0) 0 ( 0.0)
C00.6 Lip: Commissure 0 ( 0.0) 0 ( 0.0)
C00.8 Lip: Overlapping 0 ( 0.0) 0 ( 0.0)
C00.9 Lip NOS 0 ( 0.0) 0 ( 0.0)
C01.9 Tongue: Base NOS 0 ( 0.0) 0 ( 0.0)
C02.0 Tongue: Dorsal NOS 0 ( 0.0) 0 ( 0.0)
C02.1 Tongue: Border, Tip 0 ( 0.0) 0 ( 0.0)
C02.2 Tongue: Ventral NOS 0 ( 0.0) 0 ( 0.0)
C02.3 Tongue: Anterior NOS 0 ( 0.0) 0 ( 0.0)
C02.4 Lingual Tonsil 0 ( 0.0) 0 ( 0.0)
C02.8 Tongue: Overlapping 0 ( 0.0) 0 ( 0.0)
C02.9 Tongue: NOS 0 ( 0.0) 0 ( 0.0)
C03.0 Gum: Upper 0 ( 0.0) 0 ( 0.0)
C03.1 Gum: Lower 0 ( 0.0) 0 ( 0.0)
C03.9 Gum NOS 0 ( 0.0) 0 ( 0.0)
C04.0 Mouth: Anterior Floor 0 ( 0.0) 0 ( 0.0)
C04.1 Mouth: Lateral Floor 0 ( 0.0) 0 ( 0.0)
C04.9 Floor of Mouth NOS 0 ( 0.0) 0 ( 0.0)
C05.0 Hard Palate 0 ( 0.0) 0 ( 0.0)
C05.1 Soft Palate NOS 0 ( 0.0) 0 ( 0.0)
C05.2 Uvula 0 ( 0.0) 0 ( 0.0)
C05.8 Palate: Overlapping 0 ( 0.0) 0 ( 0.0)
C05.9 Palate NOS 0 ( 0.0) 0 ( 0.0)
C06.0 Cheek Mucosa 0 ( 0.0) 0 ( 0.0)
C06.1 Mouth: Vestibule 0 ( 0.0) 0 ( 0.0)
C06.2 Retromolar Area 0 ( 0.0) 0 ( 0.0)
C06.8 Mouth: Other Overlapping 0 ( 0.0) 0 ( 0.0)
C06.9 Mouth NOS 0 ( 0.0) 0 ( 0.0)
C07.9 Parotid Gland 0 ( 0.0) 0 ( 0.0)
C09.8 Tonsil: Overlapping 0 ( 0.0) 0 ( 0.0)
C09.9 Tonsil NOS 0 ( 0.0) 0 ( 0.0)
C11.1 Nasopharynx: Poster Wall 0 ( 0.0) 0 ( 0.0)
C14.2 Waldeyer Ring 0 ( 0.0) 0 ( 0.0)
C30.0 Nasal Cavity 0 ( 0.0) 0 ( 0.0)
C37.9 Thymus 0 ( 0.0) 0 ( 0.0)
C42.0 Blood 0 ( 0.0) 0 ( 0.0)
C42.2 Spleen 0 ( 0.0) 0 ( 0.0)
C42.4 Hematopoietic NOS 0 ( 0.0) 0 ( 0.0)
C44.0 Skin of lip, NOS 0 ( 0.0) 0 ( 0.0)
C44.1 Eyelid 0 ( 0.0) 0 ( 0.0)
C44.2 External ear 2 ( 0.2) 0 ( 0.0)
C44.3 Skin of ear and unspecified parts of face 2 ( 0.2) 0 ( 0.0)
C44.4 Skin of scalp and neck 3 ( 0.2) 0 ( 0.0)
C44.5 Skin of trunk 241 ( 19.0) 44 ( 58.7)
C44.6 Skin of upper limb and shoulder 7 ( 0.6) 0 ( 0.0)
C44.7 Skin of lower limb and hip 11 ( 0.9) 1 ( 1.3)
C44.8 Overlapping lesion of skin 10 ( 0.8) 1 ( 1.3)
C44.9 Skin, NOS 14 ( 1.1) 3 ( 4.0)
C50.0 Nipple 0 ( 0.0) 0 ( 0.0)
C51.0 Labium majus 91 ( 7.2) 1 ( 1.3)
C51.1 Labium minus 15 ( 1.2) 0 ( 0.0)
C51.2 Clitoris 2 ( 0.2) 0 ( 0.0)
C51.8 Overlapping lesion of vulva 59 ( 4.7) 1 ( 1.3)
C51.9 Vulva, NOS 789 ( 62.2) 20 ( 26.7)
C52.9 Vagina, NOS 1 ( 0.1) 0 ( 0.0)
C60.0 Prepuce 0 ( 0.0) 0 ( 0.0)
C60.1 Glans penis 0 ( 0.0) 0 ( 0.0)
C60.2 Body of penis 1 ( 0.1) 0 ( 0.0)
C60.8 Overlapping lesion of penis 2 ( 0.2) 0 ( 0.0)
C60.9 Penis 18 ( 1.4) 4 ( 5.3)
C63.2 Scrotum, NOS 0 ( 0.0) 0 ( 0.0)
C77.0 Lymph Nodes: HeadFaceNeck 0 ( 0.0) 0 ( 0.0)
C77.1 Intrathoracic Lymph Nodes 0 ( 0.0) 0 ( 0.0)
C77.2 Intra-abdominal LymphNodes 0 ( 0.0) 0 ( 0.0)
C77.3 Lymph Nodes of axilla or arm 0 ( 0.0) 0 ( 0.0)
C77.4 Lymph Nodes: Leg 0 ( 0.0) 0 ( 0.0)
C77.5 Pelvic Lymph Nodes 0 ( 0.0) 0 ( 0.0)
C77.8 Lymph Nodes: multiple region 0 ( 0.0) 0 ( 0.0)
C77.9 Lymph Node NOS 0 ( 0.0) 0 ( 0.0)
BEHAVIOR (%) 2 0 ( 0.0) 0 ( 0.0) NaN
3 1268 (100.0) 75 (100.0)
GRADE_F (%) Gr I: Well Diff 36 ( 2.8) 0 ( 0.0) NaN
Gr II: Mod Diff 26 ( 2.1) 3 ( 4.0)
Gr III: Poor Diff 29 ( 2.3) 7 ( 9.3)
Gr IV: Undiff/Anaplastic 0 ( 0.0) 0 ( 0.0)
5 0 ( 0.0) 0 ( 0.0)
6 0 ( 0.0) 0 ( 0.0)
7 0 ( 0.0) 0 ( 0.0)
8 0 ( 0.0) 0 ( 0.0)
NA/Unkown 1177 ( 92.8) 65 ( 86.7)
DX_STAGING_PROC_DAYS (mean (sd)) 1.76 (12.49) 5.19 (31.00) 0.078
TNM_CLIN_T (%) N_A 0 ( 0.0) 0 ( 0.0) NaN
c0 6 ( 0.5) 0 ( 0.0)
c1 133 ( 10.5) 6 ( 8.0)
c1A 140 ( 11.0) 7 ( 9.3)
c1B 135 ( 10.6) 2 ( 2.7)
c1C 0 ( 0.0) 0 ( 0.0)
c1MI 0 ( 0.0) 0 ( 0.0)
c2 160 ( 12.6) 15 ( 20.0)
c2A 0 ( 0.0) 0 ( 0.0)
c2B 0 ( 0.0) 0 ( 0.0)
c2C 0 ( 0.0) 0 ( 0.0)
c2D 0 ( 0.0) 0 ( 0.0)
c3 21 ( 1.7) 4 ( 5.3)
c3A 0 ( 0.0) 0 ( 0.0)
c3B 0 ( 0.0) 0 ( 0.0)
c4 0 ( 0.0) 1 ( 1.3)
c4A 0 ( 0.0) 0 ( 0.0)
c4B 0 ( 0.0) 0 ( 0.0)
c4C 0 ( 0.0) 0 ( 0.0)
c4D 0 ( 0.0) 0 ( 0.0)
cX 576 ( 45.4) 37 ( 49.3)
pA 0 ( 0.0) 0 ( 0.0)
pIS 27 ( 2.1) 1 ( 1.3)
NA 70 ( 5.5) 2 ( 2.7)
TNM_CLIN_N (%) N_A 0 ( 0.0) 0 ( 0.0) NaN
c0 828 ( 65.3) 38 ( 50.7)
c1 4 ( 0.3) 0 ( 0.0)
c1A 0 ( 0.0) 0 ( 0.0)
c1B 0 ( 0.0) 0 ( 0.0)
c2 2 ( 0.2) 2 ( 2.7)
c2A 0 ( 0.0) 0 ( 0.0)
c2B 1 ( 0.1) 2 ( 2.7)
c2C 1 ( 0.1) 0 ( 0.0)
c3 0 ( 0.0) 0 ( 0.0)
c3A 0 ( 0.0) 0 ( 0.0)
c3B 0 ( 0.0) 0 ( 0.0)
c3C 0 ( 0.0) 0 ( 0.0)
c4 0 ( 0.0) 0 ( 0.0)
cX 372 ( 29.3) 31 ( 41.3)
NA 60 ( 4.7) 2 ( 2.7)
TNM_CLIN_M (%) N_A 0 ( 0.0) 0 ( 0.0) NaN
c0 1163 ( 91.7) 69 ( 92.0)
c0I+ 0 ( 0.0) 0 ( 0.0)
c1 8 ( 0.6) 3 ( 4.0)
c1A 0 ( 0.0) 0 ( 0.0)
c1B 0 ( 0.0) 0 ( 0.0)
c1C 0 ( 0.0) 0 ( 0.0)
NA 97 ( 7.6) 3 ( 4.0)
TNM_CLIN_STAGE_GROUP (%) 0 61 ( 4.8) 2 ( 2.7) NaN
1 187 ( 14.7) 8 ( 10.7)
1A 105 ( 8.3) 4 ( 5.3)
1B 116 ( 9.1) 2 ( 2.7)
1C 0 ( 0.0) 0 ( 0.0)
2 166 ( 13.1) 17 ( 22.7)
2A 0 ( 0.0) 0 ( 0.0)
2B 0 ( 0.0) 0 ( 0.0)
2C 0 ( 0.0) 0 ( 0.0)
3 14 ( 1.1) 1 ( 1.3)
3A 0 ( 0.0) 0 ( 0.0)
3B 0 ( 0.0) 1 ( 1.3)
3C 0 ( 0.0) 0 ( 0.0)
4 6 ( 0.5) 3 ( 4.0)
4A 2 ( 0.2) 0 ( 0.0)
4A1 0 ( 0.0) 0 ( 0.0)
4A2 0 ( 0.0) 0 ( 0.0)
4B 3 ( 0.2) 2 ( 2.7)
4C 0 ( 0.0) 0 ( 0.0)
N_A 0 ( 0.0) 0 ( 0.0)
99 608 ( 47.9) 35 ( 46.7)
TNM_PATH_T (%) N_A 0 ( 0.0) 0 ( 0.0) NaN
p0 9 ( 0.7) 0 ( 0.0)
p1 96 ( 7.6) 1 ( 1.3)
p1A 146 ( 11.5) 1 ( 1.3)
p1B 151 ( 11.9) 3 ( 4.0)
p1C 0 ( 0.0) 0 ( 0.0)
p1MI 0 ( 0.0) 0 ( 0.0)
p2 140 ( 11.0) 4 ( 5.3)
p2A 0 ( 0.0) 0 ( 0.0)
p2B 0 ( 0.0) 0 ( 0.0)
p2C 0 ( 0.0) 0 ( 0.0)
p2D 0 ( 0.0) 0 ( 0.0)
p3 22 ( 1.7) 0 ( 0.0)
p3A 0 ( 0.0) 0 ( 0.0)
p3B 0 ( 0.0) 0 ( 0.0)
p4 1 ( 0.1) 0 ( 0.0)
p4A 0 ( 0.0) 0 ( 0.0)
p4B 0 ( 0.0) 0 ( 0.0)
p4C 0 ( 0.0) 0 ( 0.0)
p4D 0 ( 0.0) 0 ( 0.0)
pA 0 ( 0.0) 0 ( 0.0)
pIS 23 ( 1.8) 0 ( 0.0)
pX 543 ( 42.8) 47 ( 62.7)
NA 137 ( 10.8) 19 ( 25.3)
TNM_PATH_N (%) N_A 0 ( 0.0) 0 ( 0.0) NaN
p0 280 ( 22.1) 0 ( 0.0)
p0I- 0 ( 0.0) 0 ( 0.0)
p0I+ 0 ( 0.0) 0 ( 0.0)
p0M- 0 ( 0.0) 0 ( 0.0)
p0M+ 0 ( 0.0) 0 ( 0.0)
p1 6 ( 0.5) 3 ( 4.0)
p1A 0 ( 0.0) 1 ( 1.3)
p1B 1 ( 0.1) 0 ( 0.0)
p1C 0 ( 0.0) 0 ( 0.0)
p1MI 0 ( 0.0) 0 ( 0.0)
p2 1 ( 0.1) 0 ( 0.0)
p2A 0 ( 0.0) 0 ( 0.0)
p2B 0 ( 0.0) 1 ( 1.3)
p2C 2 ( 0.2) 3 ( 4.0)
p3 1 ( 0.1) 0 ( 0.0)
p3A 0 ( 0.0) 0 ( 0.0)
p3B 0 ( 0.0) 0 ( 0.0)
p3C 0 ( 0.0) 0 ( 0.0)
p4 0 ( 0.0) 0 ( 0.0)
pX 784 ( 61.8) 48 ( 64.0)
NA 193 ( 15.2) 19 ( 25.3)
TNM_PATH_M (%) N_A 0 ( 0.0) 0 ( 0.0) NaN
p0 0 ( 0.0) 0 ( 0.0)
p1 1 ( 0.1) 2 ( 2.7)
p1A 0 ( 0.0) 0 ( 0.0)
p1B 0 ( 0.0) 0 ( 0.0)
p1C 0 ( 0.0) 0 ( 0.0)
pX 576 ( 45.4) 36 ( 48.0)
NA 691 ( 54.5) 37 ( 49.3)
TNM_PATH_STAGE_GROUP (%) 0 55 ( 4.3) 0 ( 0.0) NaN
1 106 ( 8.4) 1 ( 1.3)
1A 104 ( 8.2) 0 ( 0.0)
1B 91 ( 7.2) 0 ( 0.0)
1C 0 ( 0.0) 0 ( 0.0)
2 117 ( 9.2) 2 ( 2.7)
2A 0 ( 0.0) 0 ( 0.0)
2B 0 ( 0.0) 0 ( 0.0)
2C 0 ( 0.0) 0 ( 0.0)
3 23 ( 1.8) 3 ( 4.0)
3A 1 ( 0.1) 1 ( 1.3)
3B 0 ( 0.0) 0 ( 0.0)
3C 1 ( 0.1) 3 ( 4.0)
4 1 ( 0.1) 3 ( 4.0)
4A 4 ( 0.3) 1 ( 1.3)
4A1 0 ( 0.0) 0 ( 0.0)
4B 1 ( 0.1) 0 ( 0.0)
4C 0 ( 0.0) 0 ( 0.0)
N_A 0 ( 0.0) 0 ( 0.0)
99 695 ( 54.8) 51 ( 68.0)
NA 69 ( 5.4) 10 ( 13.3)
DX_RX_STARTED_DAYS (mean (sd)) 46.61 (118.63) 97.31 (285.47) 0.002
DX_SURG_STARTED_DAYS (mean (sd)) 47.29 (120.66) 28.05 (33.21) 0.455
DX_DEFSURG_STARTED_DAYS (mean (sd)) 58.74 (135.79) 56.64 (67.82) 0.942
MARGINS (%) No Residual 586 ( 46.2) 11 ( 14.7) <0.001
Residual, NOS 158 ( 12.5) 3 ( 4.0)
Microscopic Resid 337 ( 26.6) 7 ( 9.3)
Macroscopic Resid 17 ( 1.3) 0 ( 0.0)
Not evaluable 21 ( 1.7) 1 ( 1.3)
No surg 109 ( 8.6) 52 ( 69.3)
Unknown 40 ( 3.2) 1 ( 1.3)
MARGINS_YN (%) No 586 ( 46.2) 11 ( 14.7) <0.001
Yes 512 ( 40.4) 10 ( 13.3)
No surg/Unk/NA 170 ( 13.4) 54 ( 72.0)
SURG_DISCHARGE_DAYS (mean (sd)) 2.10 (6.85) 4.26 (12.17) 0.180
READM_HOSP_30_DAYS_F (%) No_Surg_or_No_Readmit 1206 ( 95.1) 72 ( 96.0) 0.485
Unplan_Readmit_Same 34 ( 2.7) 1 ( 1.3)
Plan_Readmit_Same 15 ( 1.2) 0 ( 0.0)
PlanUnplan_Same 1 ( 0.1) 0 ( 0.0)
9 12 ( 0.9) 2 ( 2.7)
RX_SUMM_RADIATION_F (%) None 1268 (100.0) 0 ( 0.0) NaN
Beam Radiation 0 ( 0.0) 73 ( 97.3)
Radioactive Implants 0 ( 0.0) 1 ( 1.3)
Radioisotopes 0 ( 0.0) 0 ( 0.0)
Beam + Imp or Isotopes 0 ( 0.0) 1 ( 1.3)
Radiation, NOS 0 ( 0.0) 0 ( 0.0)
Unknown 0 ( 0.0) 0 ( 0.0)
PUF_30_DAY_MORT_CD_F (%) Alive_30 1119 ( 88.2) 23 ( 30.7) <0.001
Dead_30 5 ( 0.4) 0 ( 0.0)
Unknown 32 ( 2.5) 0 ( 0.0)
NA 112 ( 8.8) 52 ( 69.3)
PUF_90_DAY_MORT_CD_F (%) Alive_90 1097 ( 86.5) 23 ( 30.7) <0.001
Dead_90 10 ( 0.8) 0 ( 0.0)
Unknown 49 ( 3.9) 0 ( 0.0)
NA 112 ( 8.8) 52 ( 69.3)
DX_LASTCONTACT_DEATH_MONTHS (mean (sd)) 58.46 (40.16) 45.50 (36.80) 0.006
LYMPH_VASCULAR_INVASION_F (%) Neg_LymphVasc_Inv 261 ( 20.6) 8 ( 10.7) 0.213
Pos_LumphVasc_Inv 24 ( 1.9) 3 ( 4.0)
N_A 1 ( 0.1) 0 ( 0.0)
Unknown 406 ( 32.0) 28 ( 37.3)
NA 576 ( 45.4) 36 ( 48.0)
RX_HOSP_SURG_APPR_2010_F (%) No_Surg 90 ( 7.1) 31 ( 41.3) NaN
Robot_Assist 2 ( 0.2) 0 ( 0.0)
Robot_to_Open 0 ( 0.0) 0 ( 0.0)
Endo_Lap 18 ( 1.4) 0 ( 0.0)
Endo_Lap_to_Open 2 ( 0.2) 0 ( 0.0)
Open_Unknown 580 ( 45.7) 8 ( 10.7)
Unknown 0 ( 0.0) 0 ( 0.0)
NA 576 ( 45.4) 36 ( 48.0)
SURG_RAD_SEQ (%) Surg Alone 1158 ( 91.3) 0 ( 0.0) NaN
Surg then Rad 0 ( 0.0) 23 ( 30.7)
Rad Alone 0 ( 0.0) 52 ( 69.3)
No Treatment 105 ( 8.3) 0 ( 0.0)
Other 5 ( 0.4) 0 ( 0.0)
Rad before and after Surg 0 ( 0.0) 0 ( 0.0)
Rad then Surg 0 ( 0.0) 0 ( 0.0)
SURG_RAD_SEQ_C (%) Surg, No rad, No Chemo 1119 ( 88.2) 0 ( 0.0) NaN
Surg then Rad, No Chemo 0 ( 0.0) 20 ( 26.7)
Surg then Rad, Yes Chemo 0 ( 0.0) 3 ( 4.0)
Surg, No rad, Yes Chemo 10 ( 0.8) 0 ( 0.0)
No Surg, No Rad, Yes Chemo 9 ( 0.7) 0 ( 0.0)
No Surg, No Rad, No Chemo 94 ( 7.4) 0 ( 0.0)
Other 36 ( 2.8) 0 ( 0.0)
Rad, No Surg, Yes Chemo 0 ( 0.0) 9 ( 12.0)
Rad, No Surg, No Chemo 0 ( 0.0) 43 ( 57.3)
Rad then Surg, Yes Chemo 0 ( 0.0) 0 ( 0.0)
Rad then Surg, No Chemo 0 ( 0.0) 0 ( 0.0)
Rad before and after Surg, Yes Chemo 0 ( 0.0) 0 ( 0.0)
Rad before and after Surg, No Chemo 0 ( 0.0) 0 ( 0.0)
T_SIZE (%) No Tumor 1 ( 0.1) 0 ( 0.0) 0.092
Microscopic focus 23 ( 1.8) 1 ( 1.3)
< 1 cm 89 ( 7.0) 6 ( 8.0)
1-2 cm 94 ( 7.4) 3 ( 4.0)
2-3 cm 92 ( 7.3) 2 ( 2.7)
3-4 cm 114 ( 9.0) 2 ( 2.7)
4-5 cm 76 ( 6.0) 3 ( 4.0)
5-6 cm 63 ( 5.0) 9 ( 12.0)
>6 cm 158 ( 12.5) 12 ( 16.0)
NA_unk 558 ( 44.0) 37 ( 49.3)
SURGERY_YN (%) No 105 ( 8.3) 51 ( 68.0) <0.001
Ukn 5 ( 0.4) 1 ( 1.3)
Yes 1158 ( 91.3) 23 ( 30.7)
RADIATION_YN (%) No 1268 (100.0) 0 ( 0.0) NaN
Yes 0 ( 0.0) 75 (100.0)
NA 0 ( 0.0) 0 ( 0.0)
CHEMO_YN (%) No 1217 ( 96.0) 63 ( 84.0) <0.001
Yes 19 ( 1.5) 12 ( 16.0)
Ukn 32 ( 2.5) 0 ( 0.0)
IMMUNO_YN (%) No 1196 ( 94.3) 72 ( 96.0) 0.776
Yes 68 ( 5.4) 3 ( 4.0)
Ukn 4 ( 0.3) 0 ( 0.0)
Tx_YN (%) FALSE 56 ( 4.4) 0 ( 0.0) 0.062
TRUE 1180 ( 93.1) 75 (100.0)
NA 32 ( 2.5) 0 ( 0.0)
mets_at_dx (%) Bone 4 ( 0.3) 1 ( 1.3) NaN
Brain 0 ( 0.0) 0 ( 0.0)
Liver 0 ( 0.0) 0 ( 0.0)
Lung 0 ( 0.0) 0 ( 0.0)
None/Other/Unk/NA 1264 ( 99.7) 74 ( 98.7)
MEDICAID_EXPN_CODE (%) Non-Expansion State 454 ( 35.8) 23 ( 30.7) 0.393
Jan 2014 Expansion States 410 ( 32.3) 32 ( 42.7)
Early Expansion States (2010-13) 226 ( 17.8) 11 ( 14.7)
Late Expansion States (> Jan 2014) 164 ( 12.9) 9 ( 12.0)
Suppressed for Ages 0 - 39 14 ( 1.1) 0 ( 0.0)

p_table(data,
        vars = c("FACILITY_TYPE_F", "FACILITY_LOCATION_F", "FACILITY_GEOGRAPHY",  "AGE", "AGE_F", "AGE_40",
                 "SEX_F", "RACE_F", "HISPANIC", "INSURANCE_F", 
                 "INCOME_F", "EDUCATION_F", "U_R_F", "CROWFLY", "CDCC_TOTAL_BEST",
                 "SITE_TEXT", "BEHAVIOR", "GRADE_F",
                 "DX_STAGING_PROC_DAYS", "TNM_CLIN_T", "TNM_CLIN_N", "TNM_CLIN_M",
                 "TNM_CLIN_STAGE_GROUP", "TNM_PATH_T", "TNM_PATH_N", "TNM_PATH_M",
                 "TNM_PATH_STAGE_GROUP", "DX_RX_STARTED_DAYS", "DX_SURG_STARTED_DAYS",
                 "DX_DEFSURG_STARTED_DAYS", "MARGINS", "MARGINS_YN", "SURG_DISCHARGE_DAYS",
                 "READM_HOSP_30_DAYS_F", "RX_SUMM_RADIATION_F", "PUF_30_DAY_MORT_CD_F",
                 "PUF_90_DAY_MORT_CD_F", "DX_LASTCONTACT_DEATH_MONTHS", 
                 "LYMPH_VASCULAR_INVASION_F", "RX_HOSP_SURG_APPR_2010_F", "SURG_RAD_SEQ",
                 "SURG_RAD_SEQ_C", "T_SIZE", "SURGERY_YN", "RADIATION_YN", 
                 "CHEMO_YN", "IMMUNO_YN", "Tx_YN","mets_at_dx",
                 "MEDICAID_EXPN_CODE"), 
        strata = "CHEMO_YN")

level No Yes Ukn p test
n 1287 31 42
FACILITY_TYPE_F (%) Community Cancer Program 30 ( 2.3) 1 ( 3.2) 0 ( 0.0) 0.459
Comprehensive Comm Ca Program 434 ( 33.7) 10 ( 32.3) 18 ( 42.9)
Academic/Research Program 615 ( 47.8) 19 ( 61.3) 20 ( 47.6)
Integrated Network Ca Program 194 ( 15.1) 1 ( 3.2) 4 ( 9.5)
NA 14 ( 1.1) 0 ( 0.0) 0 ( 0.0)
FACILITY_LOCATION_F (%) New England 56 ( 4.4) 1 ( 3.2) 2 ( 4.8) 0.029
Middle Atlantic 210 ( 16.3) 8 ( 25.8) 13 ( 31.0)
South Atlantic 248 ( 19.3) 2 ( 6.5) 5 ( 11.9)
East North Central 214 ( 16.6) 5 ( 16.1) 3 ( 7.1)
East South Central 87 ( 6.8) 0 ( 0.0) 2 ( 4.8)
West North Central 135 ( 10.5) 4 ( 12.9) 2 ( 4.8)
West South Central 92 ( 7.1) 1 ( 3.2) 6 ( 14.3)
Mountain 73 ( 5.7) 1 ( 3.2) 5 ( 11.9)
Pacific 158 ( 12.3) 9 ( 29.0) 4 ( 9.5)
NA 14 ( 1.1) 0 ( 0.0) 0 ( 0.0)
FACILITY_GEOGRAPHY (%) Northeast 266 ( 20.7) 9 ( 29.0) 15 ( 35.7) 0.034
South 340 ( 26.4) 3 ( 9.7) 11 ( 26.2)
Midwest 436 ( 33.9) 9 ( 29.0) 7 ( 16.7)
West 231 ( 17.9) 10 ( 32.3) 9 ( 21.4)
NA 14 ( 1.1) 0 ( 0.0) 0 ( 0.0)
AGE (mean (sd)) 70.51 (11.86) 70.84 (11.49) 70.43 (12.81) 0.987
AGE_F (%) (0,54] 136 ( 10.6) 2 ( 6.5) 6 ( 14.3) 0.928
(54,64] 242 ( 18.8) 5 ( 16.1) 7 ( 16.7)
(64,74] 396 ( 30.8) 12 ( 38.7) 13 ( 31.0)
(74,100] 513 ( 39.9) 12 ( 38.7) 16 ( 38.1)
AGE_40 (%) (0,40] 16 ( 1.2) 1 ( 3.2) 1 ( 2.4) 0.527
(40,100] 1271 ( 98.8) 30 ( 96.8) 41 ( 97.6)
SEX_F (%) Male 239 ( 18.6) 15 ( 48.4) 7 ( 16.7) <0.001
Female 1048 ( 81.4) 16 ( 51.6) 35 ( 83.3)
RACE_F (%) White 1168 ( 90.8) 25 ( 80.6) 36 ( 85.7) 0.135
Black 22 ( 1.7) 2 ( 6.5) 1 ( 2.4)
Other/Unk 33 ( 2.6) 0 ( 0.0) 2 ( 4.8)
Asian 64 ( 5.0) 4 ( 12.9) 3 ( 7.1)
HISPANIC (%) No 1172 ( 91.1) 27 ( 87.1) 35 ( 83.3) 0.353
Yes 48 ( 3.7) 2 ( 6.5) 2 ( 4.8)
Unknown 67 ( 5.2) 2 ( 6.5) 5 ( 11.9)
INSURANCE_F (%) Private 430 ( 33.4) 10 ( 32.3) 13 ( 31.0) 0.487
None 22 ( 1.7) 1 ( 3.2) 0 ( 0.0)
Medicaid 19 ( 1.5) 2 ( 6.5) 1 ( 2.4)
Medicare 794 ( 61.7) 18 ( 58.1) 26 ( 61.9)
Other Government 7 ( 0.5) 0 ( 0.0) 1 ( 2.4)
Unknown 15 ( 1.2) 0 ( 0.0) 1 ( 2.4)
INCOME_F (%) Less than $38,000 154 ( 12.0) 4 ( 12.9) 7 ( 16.7) 0.989
$38,000 - $47,999 311 ( 24.2) 6 ( 19.4) 9 ( 21.4)
$48,000 - $62,999 346 ( 26.9) 9 ( 29.0) 10 ( 23.8)
$63,000 + 471 ( 36.6) 12 ( 38.7) 16 ( 38.1)
NA 5 ( 0.4) 0 ( 0.0) 0 ( 0.0)
EDUCATION_F (%) 21% or more 159 ( 12.4) 5 ( 16.1) 3 ( 7.1) 0.717
13 - 20.9% 300 ( 23.3) 8 ( 25.8) 12 ( 28.6)
7 - 12.9% 445 ( 34.6) 10 ( 32.3) 10 ( 23.8)
Less than 7% 378 ( 29.4) 8 ( 25.8) 17 ( 40.5)
NA 5 ( 0.4) 0 ( 0.0) 0 ( 0.0)
U_R_F (%) Metro 1043 ( 81.0) 26 ( 83.9) 37 ( 88.1) 0.485
Urban 181 ( 14.1) 3 ( 9.7) 3 ( 7.1)
Rural 27 ( 2.1) 0 ( 0.0) 0 ( 0.0)
NA 36 ( 2.8) 2 ( 6.5) 2 ( 4.8)
CROWFLY (mean (sd)) 42.15 (127.44) 22.61 (35.37) 24.20 (48.34) 0.461
CDCC_TOTAL_BEST (%) 0 1048 ( 81.4) 23 ( 74.2) 35 ( 83.3) 0.145
1 188 ( 14.6) 4 ( 12.9) 6 ( 14.3)
2 37 ( 2.9) 2 ( 6.5) 1 ( 2.4)
3 14 ( 1.1) 2 ( 6.5) 0 ( 0.0)
SITE_TEXT (%) C00.0 External Lip: Upper NOS 0 ( 0.0) 0 ( 0.0) 0 ( 0.0) NaN
C00.1 External Lip: Lower NOS 0 ( 0.0) 0 ( 0.0) 0 ( 0.0)
C00.2 External Lip: NOS 0 ( 0.0) 0 ( 0.0) 0 ( 0.0)
C00.3 Lip: Upper Mucosa 0 ( 0.0) 0 ( 0.0) 0 ( 0.0)
C00.4 Lip: Lower Mucosa 0 ( 0.0) 0 ( 0.0) 0 ( 0.0)
C00.5 Lip: Mucosa NOS 0 ( 0.0) 0 ( 0.0) 0 ( 0.0)
C00.6 Lip: Commissure 0 ( 0.0) 0 ( 0.0) 0 ( 0.0)
C00.8 Lip: Overlapping 0 ( 0.0) 0 ( 0.0) 0 ( 0.0)
C00.9 Lip NOS 0 ( 0.0) 0 ( 0.0) 0 ( 0.0)
C01.9 Tongue: Base NOS 0 ( 0.0) 0 ( 0.0) 0 ( 0.0)
C02.0 Tongue: Dorsal NOS 0 ( 0.0) 0 ( 0.0) 0 ( 0.0)
C02.1 Tongue: Border, Tip 0 ( 0.0) 0 ( 0.0) 0 ( 0.0)
C02.2 Tongue: Ventral NOS 0 ( 0.0) 0 ( 0.0) 0 ( 0.0)
C02.3 Tongue: Anterior NOS 0 ( 0.0) 0 ( 0.0) 0 ( 0.0)
C02.4 Lingual Tonsil 0 ( 0.0) 0 ( 0.0) 0 ( 0.0)
C02.8 Tongue: Overlapping 0 ( 0.0) 0 ( 0.0) 0 ( 0.0)
C02.9 Tongue: NOS 0 ( 0.0) 0 ( 0.0) 0 ( 0.0)
C03.0 Gum: Upper 0 ( 0.0) 0 ( 0.0) 0 ( 0.0)
C03.1 Gum: Lower 0 ( 0.0) 0 ( 0.0) 0 ( 0.0)
C03.9 Gum NOS 0 ( 0.0) 0 ( 0.0) 0 ( 0.0)
C04.0 Mouth: Anterior Floor 0 ( 0.0) 0 ( 0.0) 0 ( 0.0)
C04.1 Mouth: Lateral Floor 0 ( 0.0) 0 ( 0.0) 0 ( 0.0)
C04.9 Floor of Mouth NOS 0 ( 0.0) 0 ( 0.0) 0 ( 0.0)
C05.0 Hard Palate 0 ( 0.0) 0 ( 0.0) 0 ( 0.0)
C05.1 Soft Palate NOS 0 ( 0.0) 0 ( 0.0) 0 ( 0.0)
C05.2 Uvula 0 ( 0.0) 0 ( 0.0) 0 ( 0.0)
C05.8 Palate: Overlapping 0 ( 0.0) 0 ( 0.0) 0 ( 0.0)
C05.9 Palate NOS 0 ( 0.0) 0 ( 0.0) 0 ( 0.0)
C06.0 Cheek Mucosa 0 ( 0.0) 0 ( 0.0) 0 ( 0.0)
C06.1 Mouth: Vestibule 0 ( 0.0) 0 ( 0.0) 0 ( 0.0)
C06.2 Retromolar Area 0 ( 0.0) 0 ( 0.0) 0 ( 0.0)
C06.8 Mouth: Other Overlapping 0 ( 0.0) 0 ( 0.0) 0 ( 0.0)
C06.9 Mouth NOS 0 ( 0.0) 0 ( 0.0) 0 ( 0.0)
C07.9 Parotid Gland 0 ( 0.0) 0 ( 0.0) 0 ( 0.0)
C09.8 Tonsil: Overlapping 0 ( 0.0) 0 ( 0.0) 0 ( 0.0)
C09.9 Tonsil NOS 0 ( 0.0) 0 ( 0.0) 0 ( 0.0)
C11.1 Nasopharynx: Poster Wall 0 ( 0.0) 0 ( 0.0) 0 ( 0.0)
C14.2 Waldeyer Ring 0 ( 0.0) 0 ( 0.0) 0 ( 0.0)
C30.0 Nasal Cavity 0 ( 0.0) 0 ( 0.0) 0 ( 0.0)
C37.9 Thymus 0 ( 0.0) 0 ( 0.0) 0 ( 0.0)
C42.0 Blood 0 ( 0.0) 0 ( 0.0) 0 ( 0.0)
C42.2 Spleen 0 ( 0.0) 0 ( 0.0) 0 ( 0.0)
C42.4 Hematopoietic NOS 0 ( 0.0) 0 ( 0.0) 0 ( 0.0)
C44.0 Skin of lip, NOS 0 ( 0.0) 0 ( 0.0) 0 ( 0.0)
C44.1 Eyelid 0 ( 0.0) 0 ( 0.0) 0 ( 0.0)
C44.2 External ear 2 ( 0.2) 0 ( 0.0) 0 ( 0.0)
C44.3 Skin of ear and unspecified parts of face 2 ( 0.2) 0 ( 0.0) 0 ( 0.0)
C44.4 Skin of scalp and neck 3 ( 0.2) 0 ( 0.0) 0 ( 0.0)
C44.5 Skin of trunk 264 ( 20.5) 17 ( 54.8) 6 ( 14.3)
C44.6 Skin of upper limb and shoulder 7 ( 0.5) 0 ( 0.0) 0 ( 0.0)
C44.7 Skin of lower limb and hip 9 ( 0.7) 1 ( 3.2) 2 ( 4.8)
C44.8 Overlapping lesion of skin 10 ( 0.8) 0 ( 0.0) 1 ( 2.4)
C44.9 Skin, NOS 17 ( 1.3) 0 ( 0.0) 2 ( 4.8)
C50.0 Nipple 0 ( 0.0) 0 ( 0.0) 0 ( 0.0)
C51.0 Labium majus 91 ( 7.1) 0 ( 0.0) 1 ( 2.4)
C51.1 Labium minus 15 ( 1.2) 0 ( 0.0) 0 ( 0.0)
C51.2 Clitoris 2 ( 0.2) 0 ( 0.0) 0 ( 0.0)
C51.8 Overlapping lesion of vulva 58 ( 4.5) 1 ( 3.2) 1 ( 2.4)
C51.9 Vulva, NOS 782 ( 60.8) 11 ( 35.5) 29 ( 69.0)
C52.9 Vagina, NOS 1 ( 0.1) 0 ( 0.0) 0 ( 0.0)
C60.0 Prepuce 0 ( 0.0) 0 ( 0.0) 0 ( 0.0)
C60.1 Glans penis 0 ( 0.0) 0 ( 0.0) 0 ( 0.0)
C60.2 Body of penis 1 ( 0.1) 0 ( 0.0) 0 ( 0.0)
C60.8 Overlapping lesion of penis 1 ( 0.1) 1 ( 3.2) 0 ( 0.0)
C60.9 Penis 22 ( 1.7) 0 ( 0.0) 0 ( 0.0)
C63.2 Scrotum, NOS 0 ( 0.0) 0 ( 0.0) 0 ( 0.0)
C77.0 Lymph Nodes: HeadFaceNeck 0 ( 0.0) 0 ( 0.0) 0 ( 0.0)
C77.1 Intrathoracic Lymph Nodes 0 ( 0.0) 0 ( 0.0) 0 ( 0.0)
C77.2 Intra-abdominal LymphNodes 0 ( 0.0) 0 ( 0.0) 0 ( 0.0)
C77.3 Lymph Nodes of axilla or arm 0 ( 0.0) 0 ( 0.0) 0 ( 0.0)
C77.4 Lymph Nodes: Leg 0 ( 0.0) 0 ( 0.0) 0 ( 0.0)
C77.5 Pelvic Lymph Nodes 0 ( 0.0) 0 ( 0.0) 0 ( 0.0)
C77.8 Lymph Nodes: multiple region 0 ( 0.0) 0 ( 0.0) 0 ( 0.0)
C77.9 Lymph Node NOS 0 ( 0.0) 0 ( 0.0) 0 ( 0.0)
BEHAVIOR (%) 2 0 ( 0.0) 0 ( 0.0) 0 ( 0.0) NaN
3 1287 (100.0) 31 (100.0) 42 (100.0)
GRADE_F (%) Gr I: Well Diff 37 ( 2.9) 0 ( 0.0) 1 ( 2.4) NaN
Gr II: Mod Diff 27 ( 2.1) 2 ( 6.5) 1 ( 2.4)
Gr III: Poor Diff 30 ( 2.3) 6 ( 19.4) 0 ( 0.0)
Gr IV: Undiff/Anaplastic 0 ( 0.0) 0 ( 0.0) 0 ( 0.0)
5 0 ( 0.0) 0 ( 0.0) 0 ( 0.0)
6 0 ( 0.0) 0 ( 0.0) 0 ( 0.0)
7 0 ( 0.0) 0 ( 0.0) 0 ( 0.0)
8 0 ( 0.0) 0 ( 0.0) 0 ( 0.0)
NA/Unkown 1193 ( 92.7) 23 ( 74.2) 40 ( 95.2)
DX_STAGING_PROC_DAYS (mean (sd)) 1.86 (14.22) 3.96 (12.29) 3.50 (17.85) 0.665
TNM_CLIN_T (%) N_A 0 ( 0.0) 0 ( 0.0) 1 ( 2.4) NaN
c0 6 ( 0.5) 0 ( 0.0) 0 ( 0.0)
c1 134 ( 10.4) 3 ( 9.7) 2 ( 4.8)
c1A 144 ( 11.2) 1 ( 3.2) 3 ( 7.1)
c1B 133 ( 10.3) 0 ( 0.0) 5 ( 11.9)
c1C 0 ( 0.0) 0 ( 0.0) 0 ( 0.0)
c1MI 0 ( 0.0) 0 ( 0.0) 0 ( 0.0)
c2 169 ( 13.1) 1 ( 3.2) 6 ( 14.3)
c2A 0 ( 0.0) 0 ( 0.0) 0 ( 0.0)
c2B 0 ( 0.0) 0 ( 0.0) 0 ( 0.0)
c2C 0 ( 0.0) 0 ( 0.0) 0 ( 0.0)
c2D 0 ( 0.0) 0 ( 0.0) 0 ( 0.0)
c3 19 ( 1.5) 4 ( 12.9) 2 ( 4.8)
c3A 0 ( 0.0) 0 ( 0.0) 0 ( 0.0)
c3B 0 ( 0.0) 0 ( 0.0) 0 ( 0.0)
c4 1 ( 0.1) 0 ( 0.0) 0 ( 0.0)
c4A 0 ( 0.0) 0 ( 0.0) 0 ( 0.0)
c4B 0 ( 0.0) 0 ( 0.0) 0 ( 0.0)
c4C 0 ( 0.0) 0 ( 0.0) 0 ( 0.0)
c4D 0 ( 0.0) 0 ( 0.0) 0 ( 0.0)
cX 586 ( 45.5) 21 ( 67.7) 18 ( 42.9)
pA 0 ( 0.0) 0 ( 0.0) 0 ( 0.0)
pIS 28 ( 2.2) 0 ( 0.0) 0 ( 0.0)
NA 67 ( 5.2) 1 ( 3.2) 5 ( 11.9)
TNM_CLIN_N (%) N_A 0 ( 0.0) 0 ( 0.0) 1 ( 2.4) NaN
c0 837 ( 65.0) 14 ( 45.2) 21 ( 50.0)
c1 2 ( 0.2) 2 ( 6.5) 0 ( 0.0)
c1A 0 ( 0.0) 0 ( 0.0) 0 ( 0.0)
c1B 0 ( 0.0) 0 ( 0.0) 0 ( 0.0)
c2 2 ( 0.2) 2 ( 6.5) 0 ( 0.0)
c2A 0 ( 0.0) 0 ( 0.0) 0 ( 0.0)
c2B 1 ( 0.1) 2 ( 6.5) 0 ( 0.0)
c2C 1 ( 0.1) 0 ( 0.0) 0 ( 0.0)
c3 0 ( 0.0) 0 ( 0.0) 0 ( 0.0)
c3A 0 ( 0.0) 0 ( 0.0) 0 ( 0.0)
c3B 0 ( 0.0) 0 ( 0.0) 0 ( 0.0)
c3C 0 ( 0.0) 0 ( 0.0) 0 ( 0.0)
c4 0 ( 0.0) 0 ( 0.0) 0 ( 0.0)
cX 387 ( 30.1) 10 ( 32.3) 15 ( 35.7)
NA 57 ( 4.4) 1 ( 3.2) 5 ( 11.9)
TNM_CLIN_M (%) N_A 0 ( 0.0) 0 ( 0.0) 1 ( 2.4) NaN
c0 1191 ( 92.5) 21 ( 67.7) 36 ( 85.7)
c0I+ 0 ( 0.0) 0 ( 0.0) 0 ( 0.0)
c1 4 ( 0.3) 7 ( 22.6) 0 ( 0.0)
c1A 0 ( 0.0) 0 ( 0.0) 0 ( 0.0)
c1B 0 ( 0.0) 0 ( 0.0) 0 ( 0.0)
c1C 0 ( 0.0) 0 ( 0.0) 0 ( 0.0)
NA 92 ( 7.1) 3 ( 9.7) 5 ( 11.9)
TNM_CLIN_STAGE_GROUP (%) 0 62 ( 4.8) 0 ( 0.0) 2 ( 4.8) NaN
1 189 ( 14.7) 3 ( 9.7) 4 ( 9.5)
1A 106 ( 8.2) 1 ( 3.2) 2 ( 4.8)
1B 115 ( 8.9) 0 ( 0.0) 4 ( 9.5)
1C 0 ( 0.0) 0 ( 0.0) 0 ( 0.0)
2 174 ( 13.5) 3 ( 9.7) 7 ( 16.7)
2A 0 ( 0.0) 0 ( 0.0) 0 ( 0.0)
2B 0 ( 0.0) 0 ( 0.0) 0 ( 0.0)
2C 0 ( 0.0) 0 ( 0.0) 0 ( 0.0)
3 14 ( 1.1) 1 ( 3.2) 0 ( 0.0)
3A 0 ( 0.0) 0 ( 0.0) 0 ( 0.0)
3B 1 ( 0.1) 0 ( 0.0) 0 ( 0.0)
3C 0 ( 0.0) 0 ( 0.0) 0 ( 0.0)
4 3 ( 0.2) 6 ( 19.4) 0 ( 0.0)
4A 2 ( 0.2) 0 ( 0.0) 0 ( 0.0)
4A1 0 ( 0.0) 0 ( 0.0) 0 ( 0.0)
4A2 0 ( 0.0) 0 ( 0.0) 0 ( 0.0)
4B 3 ( 0.2) 2 ( 6.5) 0 ( 0.0)
4C 0 ( 0.0) 0 ( 0.0) 0 ( 0.0)
N_A 0 ( 0.0) 0 ( 0.0) 1 ( 2.4)
99 618 ( 48.0) 15 ( 48.4) 22 ( 52.4)
TNM_PATH_T (%) N_A 0 ( 0.0) 0 ( 0.0) 1 ( 2.4) NaN
p0 9 ( 0.7) 0 ( 0.0) 0 ( 0.0)
p1 95 ( 7.4) 0 ( 0.0) 3 ( 7.1)
p1A 144 ( 11.2) 0 ( 0.0) 3 ( 7.1)
p1B 151 ( 11.7) 1 ( 3.2) 5 ( 11.9)
p1C 0 ( 0.0) 0 ( 0.0) 0 ( 0.0)
p1MI 0 ( 0.0) 0 ( 0.0) 0 ( 0.0)
p2 138 ( 10.7) 2 ( 6.5) 4 ( 9.5)
p2A 0 ( 0.0) 0 ( 0.0) 0 ( 0.0)
p2B 0 ( 0.0) 0 ( 0.0) 0 ( 0.0)
p2C 0 ( 0.0) 0 ( 0.0) 0 ( 0.0)
p2D 0 ( 0.0) 0 ( 0.0) 0 ( 0.0)
p3 22 ( 1.7) 0 ( 0.0) 0 ( 0.0)
p3A 0 ( 0.0) 0 ( 0.0) 0 ( 0.0)
p3B 0 ( 0.0) 0 ( 0.0) 0 ( 0.0)
p4 1 ( 0.1) 0 ( 0.0) 0 ( 0.0)
p4A 0 ( 0.0) 0 ( 0.0) 0 ( 0.0)
p4B 0 ( 0.0) 0 ( 0.0) 0 ( 0.0)
p4C 0 ( 0.0) 0 ( 0.0) 0 ( 0.0)
p4D 0 ( 0.0) 0 ( 0.0) 0 ( 0.0)
pA 0 ( 0.0) 0 ( 0.0) 0 ( 0.0)
pIS 23 ( 1.8) 0 ( 0.0) 0 ( 0.0)
pX 555 ( 43.1) 24 ( 77.4) 22 ( 52.4)
NA 149 ( 11.6) 4 ( 12.9) 4 ( 9.5)
TNM_PATH_N (%) N_A 0 ( 0.0) 0 ( 0.0) 1 ( 2.4) NaN
p0 272 ( 21.1) 0 ( 0.0) 10 ( 23.8)
p0I- 0 ( 0.0) 0 ( 0.0) 0 ( 0.0)
p0I+ 0 ( 0.0) 0 ( 0.0) 0 ( 0.0)
p0M- 0 ( 0.0) 0 ( 0.0) 0 ( 0.0)
p0M+ 0 ( 0.0) 0 ( 0.0) 0 ( 0.0)
p1 5 ( 0.4) 4 ( 12.9) 0 ( 0.0)
p1A 1 ( 0.1) 0 ( 0.0) 0 ( 0.0)
p1B 1 ( 0.1) 0 ( 0.0) 0 ( 0.0)
p1C 0 ( 0.0) 0 ( 0.0) 0 ( 0.0)
p1MI 0 ( 0.0) 0 ( 0.0) 0 ( 0.0)
p2 1 ( 0.1) 0 ( 0.0) 0 ( 0.0)
p2A 0 ( 0.0) 0 ( 0.0) 0 ( 0.0)
p2B 1 ( 0.1) 0 ( 0.0) 0 ( 0.0)
p2C 4 ( 0.3) 1 ( 3.2) 1 ( 2.4)
p3 1 ( 0.1) 0 ( 0.0) 0 ( 0.0)
p3A 0 ( 0.0) 0 ( 0.0) 0 ( 0.0)
p3B 0 ( 0.0) 0 ( 0.0) 0 ( 0.0)
p3C 0 ( 0.0) 0 ( 0.0) 0 ( 0.0)
p4 0 ( 0.0) 0 ( 0.0) 0 ( 0.0)
pX 797 ( 61.9) 22 ( 71.0) 25 ( 59.5)
NA 204 ( 15.9) 4 ( 12.9) 5 ( 11.9)
TNM_PATH_M (%) N_A 0 ( 0.0) 0 ( 0.0) 1 ( 2.4) NaN
p0 0 ( 0.0) 0 ( 0.0) 0 ( 0.0)
p1 2 ( 0.2) 1 ( 3.2) 0 ( 0.0)
p1A 0 ( 0.0) 0 ( 0.0) 0 ( 0.0)
p1B 0 ( 0.0) 0 ( 0.0) 0 ( 0.0)
p1C 0 ( 0.0) 0 ( 0.0) 0 ( 0.0)
pX 588 ( 45.7) 14 ( 45.2) 20 ( 47.6)
NA 697 ( 54.2) 16 ( 51.6) 21 ( 50.0)
TNM_PATH_STAGE_GROUP (%) 0 52 ( 4.0) 0 ( 0.0) 6 ( 14.3) NaN
1 102 ( 7.9) 0 ( 0.0) 5 ( 11.9)
1A 103 ( 8.0) 0 ( 0.0) 1 ( 2.4)
1B 91 ( 7.1) 0 ( 0.0) 1 ( 2.4)
1C 0 ( 0.0) 0 ( 0.0) 0 ( 0.0)
2 114 ( 8.9) 1 ( 3.2) 5 ( 11.9)
2A 0 ( 0.0) 0 ( 0.0) 0 ( 0.0)
2B 0 ( 0.0) 0 ( 0.0) 0 ( 0.0)
2C 0 ( 0.0) 0 ( 0.0) 0 ( 0.0)
3 24 ( 1.9) 2 ( 6.5) 0 ( 0.0)
3A 2 ( 0.2) 0 ( 0.0) 0 ( 0.0)
3B 0 ( 0.0) 0 ( 0.0) 0 ( 0.0)
3C 3 ( 0.2) 1 ( 3.2) 1 ( 2.4)
4 2 ( 0.2) 2 ( 6.5) 0 ( 0.0)
4A 5 ( 0.4) 0 ( 0.0) 0 ( 0.0)
4A1 0 ( 0.0) 0 ( 0.0) 0 ( 0.0)
4B 1 ( 0.1) 0 ( 0.0) 0 ( 0.0)
4C 0 ( 0.0) 0 ( 0.0) 0 ( 0.0)
N_A 0 ( 0.0) 0 ( 0.0) 1 ( 2.4)
99 711 ( 55.2) 24 ( 77.4) 21 ( 50.0)
NA 77 ( 6.0) 1 ( 3.2) 1 ( 2.4)
DX_RX_STARTED_DAYS (mean (sd)) 49.99 (136.83) 42.11 (41.83) 32.43 (37.31) 0.704
DX_SURG_STARTED_DAYS (mean (sd)) 47.20 (121.12) 43.67 (64.86) 32.69 (37.80) 0.769
DX_DEFSURG_STARTED_DAYS (mean (sd)) 59.00 (136.58) 59.17 (60.00) 38.67 (38.09) 0.670
MARGINS (%) No Residual 578 ( 44.9) 5 ( 16.1) 18 ( 42.9) <0.001
Residual, NOS 158 ( 12.3) 2 ( 6.5) 4 ( 9.5)
Microscopic Resid 336 ( 26.1) 2 ( 6.5) 13 ( 31.0)
Macroscopic Resid 16 ( 1.2) 1 ( 3.2) 0 ( 0.0)
Not evaluable 20 ( 1.6) 1 ( 3.2) 1 ( 2.4)
No surg 140 ( 10.9) 18 ( 58.1) 3 ( 7.1)
Unknown 39 ( 3.0) 2 ( 6.5) 3 ( 7.1)
MARGINS_YN (%) No 578 ( 44.9) 5 ( 16.1) 18 ( 42.9) <0.001
Yes 510 ( 39.6) 5 ( 16.1) 17 ( 40.5)
No surg/Unk/NA 199 ( 15.5) 21 ( 67.7) 7 ( 16.7)
SURG_DISCHARGE_DAYS (mean (sd)) 2.08 (6.82) 8.36 (18.08) 1.31 (1.60) 0.009
READM_HOSP_30_DAYS_F (%) No_Surg_or_No_Readmit 1225 ( 95.2) 29 ( 93.5) 38 ( 90.5) 0.454
Unplan_Readmit_Same 33 ( 2.6) 1 ( 3.2) 2 ( 4.8)
Plan_Readmit_Same 15 ( 1.2) 0 ( 0.0) 0 ( 0.0)
PlanUnplan_Same 1 ( 0.1) 0 ( 0.0) 0 ( 0.0)
9 13 ( 1.0) 1 ( 3.2) 2 ( 4.8)
RX_SUMM_RADIATION_F (%) None 1217 ( 94.6) 19 ( 61.3) 32 ( 76.2) NaN
Beam Radiation 61 ( 4.7) 12 ( 38.7) 0 ( 0.0)
Radioactive Implants 1 ( 0.1) 0 ( 0.0) 0 ( 0.0)
Radioisotopes 0 ( 0.0) 0 ( 0.0) 0 ( 0.0)
Beam + Imp or Isotopes 1 ( 0.1) 0 ( 0.0) 0 ( 0.0)
Radiation, NOS 0 ( 0.0) 0 ( 0.0) 0 ( 0.0)
Unknown 7 ( 0.5) 0 ( 0.0) 10 ( 23.8)
PUF_30_DAY_MORT_CD_F (%) Alive_30 1106 ( 85.9) 12 ( 38.7) 38 ( 90.5) <0.001
Dead_30 5 ( 0.4) 0 ( 0.0) 0 ( 0.0)
Unknown 33 ( 2.6) 0 ( 0.0) 0 ( 0.0)
NA 143 ( 11.1) 19 ( 61.3) 4 ( 9.5)
PUF_90_DAY_MORT_CD_F (%) Alive_90 1084 ( 84.2) 12 ( 38.7) 38 ( 90.5) <0.001
Dead_90 10 ( 0.8) 0 ( 0.0) 0 ( 0.0)
Unknown 50 ( 3.9) 0 ( 0.0) 0 ( 0.0)
NA 143 ( 11.1) 19 ( 61.3) 4 ( 9.5)
DX_LASTCONTACT_DEATH_MONTHS (mean (sd)) 58.20 (40.12) 29.17 (21.03) 74.63 (45.81) <0.001
LYMPH_VASCULAR_INVASION_F (%) Neg_LymphVasc_Inv 261 ( 20.3) 2 ( 6.5) 8 ( 19.0) 0.075
Pos_LumphVasc_Inv 23 ( 1.8) 3 ( 9.7) 2 ( 4.8)
N_A 1 ( 0.1) 0 ( 0.0) 0 ( 0.0)
Unknown 414 ( 32.2) 12 ( 38.7) 12 ( 28.6)
NA 588 ( 45.7) 14 ( 45.2) 20 ( 47.6)
RX_HOSP_SURG_APPR_2010_F (%) No_Surg 107 ( 8.3) 13 ( 41.9) 2 ( 4.8) NaN
Robot_Assist 2 ( 0.2) 0 ( 0.0) 0 ( 0.0)
Robot_to_Open 0 ( 0.0) 0 ( 0.0) 0 ( 0.0)
Endo_Lap 18 ( 1.4) 0 ( 0.0) 0 ( 0.0)
Endo_Lap_to_Open 1 ( 0.1) 0 ( 0.0) 1 ( 2.4)
Open_Unknown 571 ( 44.4) 4 ( 12.9) 19 ( 45.2)
Unknown 0 ( 0.0) 0 ( 0.0) 0 ( 0.0)
NA 588 ( 45.7) 14 ( 45.2) 20 ( 47.6)
SURG_RAD_SEQ (%) Surg Alone 1119 ( 86.9) 10 ( 32.3) 29 ( 69.0) NaN
Surg then Rad 20 ( 1.6) 3 ( 9.7) 0 ( 0.0)
Rad Alone 43 ( 3.3) 9 ( 29.0) 0 ( 0.0)
No Treatment 94 ( 7.3) 9 ( 29.0) 2 ( 4.8)
Other 11 ( 0.9) 0 ( 0.0) 11 ( 26.2)
Rad before and after Surg 0 ( 0.0) 0 ( 0.0) 0 ( 0.0)
Rad then Surg 0 ( 0.0) 0 ( 0.0) 0 ( 0.0)
SURG_RAD_SEQ_C (%) Surg, No rad, No Chemo 1119 ( 86.9) 0 ( 0.0) 0 ( 0.0) NaN
Surg then Rad, No Chemo 20 ( 1.6) 0 ( 0.0) 0 ( 0.0)
Surg then Rad, Yes Chemo 0 ( 0.0) 3 ( 9.7) 0 ( 0.0)
Surg, No rad, Yes Chemo 0 ( 0.0) 10 ( 32.3) 0 ( 0.0)
No Surg, No Rad, Yes Chemo 0 ( 0.0) 9 ( 29.0) 0 ( 0.0)
No Surg, No Rad, No Chemo 94 ( 7.3) 0 ( 0.0) 0 ( 0.0)
Other 11 ( 0.9) 0 ( 0.0) 42 (100.0)
Rad, No Surg, Yes Chemo 0 ( 0.0) 9 ( 29.0) 0 ( 0.0)
Rad, No Surg, No Chemo 43 ( 3.3) 0 ( 0.0) 0 ( 0.0)
Rad then Surg, Yes Chemo 0 ( 0.0) 0 ( 0.0) 0 ( 0.0)
Rad then Surg, No Chemo 0 ( 0.0) 0 ( 0.0) 0 ( 0.0)
Rad before and after Surg, Yes Chemo 0 ( 0.0) 0 ( 0.0) 0 ( 0.0)
Rad before and after Surg, No Chemo 0 ( 0.0) 0 ( 0.0) 0 ( 0.0)
T_SIZE (%) No Tumor 1 ( 0.1) 0 ( 0.0) 0 ( 0.0) 0.104
Microscopic focus 24 ( 1.9) 0 ( 0.0) 1 ( 2.4)
< 1 cm 95 ( 7.4) 0 ( 0.0) 1 ( 2.4)
1-2 cm 94 ( 7.3) 2 ( 6.5) 1 ( 2.4)
2-3 cm 93 ( 7.2) 0 ( 0.0) 2 ( 4.8)
3-4 cm 111 ( 8.6) 0 ( 0.0) 6 ( 14.3)
4-5 cm 77 ( 6.0) 1 ( 3.2) 2 ( 4.8)
5-6 cm 69 ( 5.4) 3 ( 9.7) 0 ( 0.0)
>6 cm 162 ( 12.6) 3 ( 9.7) 10 ( 23.8)
NA_unk 561 ( 43.6) 22 ( 71.0) 19 ( 45.2)
SURGERY_YN (%) No 137 ( 10.6) 17 ( 54.8) 2 ( 4.8) <0.001
Ukn 5 ( 0.4) 1 ( 3.2) 2 ( 4.8)
Yes 1145 ( 89.0) 13 ( 41.9) 38 ( 90.5)
RADIATION_YN (%) No 1217 ( 94.6) 19 ( 61.3) 32 ( 76.2) <0.001
Yes 63 ( 4.9) 12 ( 38.7) 0 ( 0.0)
NA 7 ( 0.5) 0 ( 0.0) 10 ( 23.8)
CHEMO_YN (%) No 1287 (100.0) 0 ( 0.0) 0 ( 0.0) <0.001
Yes 0 ( 0.0) 31 (100.0) 0 ( 0.0)
Ukn 0 ( 0.0) 0 ( 0.0) 42 (100.0)
IMMUNO_YN (%) No 1219 ( 94.7) 29 ( 93.5) 31 ( 73.8) <0.001
Yes 67 ( 5.2) 2 ( 6.5) 2 ( 4.8)
Ukn 1 ( 0.1) 0 ( 0.0) 9 ( 21.4)
Tx_YN (%) FALSE 56 ( 4.4) 0 ( 0.0) 0 ( 0.0) <0.001
TRUE 1231 ( 95.6) 31 (100.0) 0 ( 0.0)
NA 0 ( 0.0) 0 ( 0.0) 42 (100.0)
mets_at_dx (%) Bone 0 ( 0.0) 5 ( 16.1) 0 ( 0.0) NaN
Brain 0 ( 0.0) 0 ( 0.0) 0 ( 0.0)
Liver 0 ( 0.0) 0 ( 0.0) 0 ( 0.0)
Lung 0 ( 0.0) 0 ( 0.0) 0 ( 0.0)
None/Other/Unk/NA 1287 (100.0) 26 ( 83.9) 42 (100.0)
MEDICAID_EXPN_CODE (%) Non-Expansion State 463 ( 36.0) 4 ( 12.9) 15 ( 35.7) 0.164
Jan 2014 Expansion States 422 ( 32.8) 14 ( 45.2) 14 ( 33.3)
Early Expansion States (2010-13) 220 ( 17.1) 10 ( 32.3) 9 ( 21.4)
Late Expansion States (> Jan 2014) 168 ( 13.1) 3 ( 9.7) 4 ( 9.5)
Suppressed for Ages 0 - 39 14 ( 1.1) 0 ( 0.0) 0 ( 0.0)

p_table(data,
        vars = c("FACILITY_TYPE_F", "FACILITY_LOCATION_F", "FACILITY_GEOGRAPHY",  "AGE", "AGE_F", "AGE_40",
                 "SEX_F", "RACE_F", "HISPANIC", "INSURANCE_F", 
                 "INCOME_F", "EDUCATION_F", "U_R_F", "CROWFLY", "CDCC_TOTAL_BEST",
                 "SITE_TEXT", "BEHAVIOR", "GRADE_F",
                 "DX_STAGING_PROC_DAYS", "TNM_CLIN_T", "TNM_CLIN_N", "TNM_CLIN_M",
                 "TNM_CLIN_STAGE_GROUP", "TNM_PATH_T", "TNM_PATH_N", "TNM_PATH_M",
                 "TNM_PATH_STAGE_GROUP", "DX_RX_STARTED_DAYS", "DX_SURG_STARTED_DAYS",
                 "DX_DEFSURG_STARTED_DAYS", "MARGINS", "MARGINS_YN", "SURG_DISCHARGE_DAYS",
                 "READM_HOSP_30_DAYS_F", "RX_SUMM_RADIATION_F", "PUF_30_DAY_MORT_CD_F",
                 "PUF_90_DAY_MORT_CD_F", "DX_LASTCONTACT_DEATH_MONTHS", 
                 "LYMPH_VASCULAR_INVASION_F", "RX_HOSP_SURG_APPR_2010_F", "SURG_RAD_SEQ",
                 "SURG_RAD_SEQ_C", "T_SIZE", "SURGERY_YN", "RADIATION_YN", 
                 "CHEMO_YN", "mets_at_dx", "IMMUNO_YN", "Tx_YN",
                 "MEDICAID_EXPN_CODE"), 
        strata = "Tx_YN")
no non-missing arguments to min; returning Infno non-missing arguments to min; returning Infno non-missing arguments to min; returning Infno non-missing arguments to min; returning Infno non-missing arguments to max; returning -Infno non-missing arguments to max; returning -Infno non-missing arguments to max; returning -Infno non-missing arguments to max; returning -InfVariable has only NA's in at least one stratum. na.rm turned off.Variable has only NA's in at least one stratum. na.rm turned off.Variable has only NA's in at least one stratum. na.rm turned off.Variable has only NA's in at least one stratum. na.rm turned off.
level FALSE TRUE p test
n 56 1262
FACILITY_TYPE_F (%) Community Cancer Program 5 ( 8.9) 26 ( 2.1) 0.008
Comprehensive Comm Ca Program 22 ( 39.3) 422 ( 33.4)
Academic/Research Program 20 ( 35.7) 614 ( 48.7)
Integrated Network Ca Program 9 ( 16.1) 186 ( 14.7)
NA 0 ( 0.0) 14 ( 1.1)
FACILITY_LOCATION_F (%) New England 4 ( 7.1) 53 ( 4.2) 0.889
Middle Atlantic 8 ( 14.3) 210 ( 16.6)
South Atlantic 8 ( 14.3) 242 ( 19.2)
East North Central 12 ( 21.4) 207 ( 16.4)
East South Central 4 ( 7.1) 83 ( 6.6)
West North Central 5 ( 8.9) 134 ( 10.6)
West South Central 3 ( 5.4) 90 ( 7.1)
Mountain 3 ( 5.4) 71 ( 5.6)
Pacific 9 ( 16.1) 158 ( 12.5)
NA 0 ( 0.0) 14 ( 1.1)
FACILITY_GEOGRAPHY (%) Northeast 12 ( 21.4) 263 ( 20.8) 0.717
South 11 ( 19.6) 332 ( 26.3)
Midwest 21 ( 37.5) 424 ( 33.6)
West 12 ( 21.4) 229 ( 18.1)
NA 0 ( 0.0) 14 ( 1.1)
AGE (mean (sd)) 78.12 (12.41) 70.18 (11.71) <0.001
AGE_F (%) (0,54] 2 ( 3.6) 136 ( 10.8) 0.001
(54,64] 8 ( 14.3) 239 ( 18.9)
(64,74] 9 ( 16.1) 399 ( 31.6)
(74,100] 37 ( 66.1) 488 ( 38.7)
AGE_40 (%) (0,40] 0 ( 0.0) 17 ( 1.3) 0.788
(40,100] 56 (100.0) 1245 ( 98.7)
SEX_F (%) Male 7 ( 12.5) 247 ( 19.6) 0.254
Female 49 ( 87.5) 1015 ( 80.4)
RACE_F (%) White 49 ( 87.5) 1144 ( 90.6) 0.726
Black 2 ( 3.6) 22 ( 1.7)
Other/Unk 2 ( 3.6) 31 ( 2.5)
Asian 3 ( 5.4) 65 ( 5.2)
HISPANIC (%) No 53 ( 94.6) 1146 ( 90.8) 0.491
Yes 2 ( 3.6) 48 ( 3.8)
Unknown 1 ( 1.8) 68 ( 5.4)
INSURANCE_F (%) Private 14 ( 25.0) 426 ( 33.8) <0.001
None 0 ( 0.0) 23 ( 1.8)
Medicaid 5 ( 8.9) 16 ( 1.3)
Medicare 34 ( 60.7) 778 ( 61.6)
Other Government 0 ( 0.0) 7 ( 0.6)
Unknown 3 ( 5.4) 12 ( 1.0)
INCOME_F (%) Less than $38,000 10 ( 17.9) 148 ( 11.7) 0.553
$38,000 - $47,999 10 ( 17.9) 307 ( 24.3)
$48,000 - $62,999 14 ( 25.0) 341 ( 27.0)
$63,000 + 22 ( 39.3) 461 ( 36.5)
NA 0 ( 0.0) 5 ( 0.4)
EDUCATION_F (%) 21% or more 8 ( 14.3) 156 ( 12.4) 0.926
13 - 20.9% 11 ( 19.6) 297 ( 23.5)
7 - 12.9% 21 ( 37.5) 434 ( 34.4)
Less than 7% 16 ( 28.6) 370 ( 29.3)
NA 0 ( 0.0) 5 ( 0.4)
U_R_F (%) Metro 43 ( 76.8) 1026 ( 81.3) 0.297
Urban 9 ( 16.1) 175 ( 13.9)
Rural 3 ( 5.4) 24 ( 1.9)
NA 1 ( 1.8) 37 ( 2.9)
CROWFLY (mean (sd)) 16.87 (21.12) 42.79 (128.67) 0.132
CDCC_TOTAL_BEST (%) 0 48 ( 85.7) 1023 ( 81.1) 0.302
1 4 ( 7.1) 188 ( 14.9)
2 3 ( 5.4) 36 ( 2.9)
3 1 ( 1.8) 15 ( 1.2)
SITE_TEXT (%) C00.0 External Lip: Upper NOS 0 ( 0.0) 0 ( 0.0) NaN
C00.1 External Lip: Lower NOS 0 ( 0.0) 0 ( 0.0)
C00.2 External Lip: NOS 0 ( 0.0) 0 ( 0.0)
C00.3 Lip: Upper Mucosa 0 ( 0.0) 0 ( 0.0)
C00.4 Lip: Lower Mucosa 0 ( 0.0) 0 ( 0.0)
C00.5 Lip: Mucosa NOS 0 ( 0.0) 0 ( 0.0)
C00.6 Lip: Commissure 0 ( 0.0) 0 ( 0.0)
C00.8 Lip: Overlapping 0 ( 0.0) 0 ( 0.0)
C00.9 Lip NOS 0 ( 0.0) 0 ( 0.0)
C01.9 Tongue: Base NOS 0 ( 0.0) 0 ( 0.0)
C02.0 Tongue: Dorsal NOS 0 ( 0.0) 0 ( 0.0)
C02.1 Tongue: Border, Tip 0 ( 0.0) 0 ( 0.0)
C02.2 Tongue: Ventral NOS 0 ( 0.0) 0 ( 0.0)
C02.3 Tongue: Anterior NOS 0 ( 0.0) 0 ( 0.0)
C02.4 Lingual Tonsil 0 ( 0.0) 0 ( 0.0)
C02.8 Tongue: Overlapping 0 ( 0.0) 0 ( 0.0)
C02.9 Tongue: NOS 0 ( 0.0) 0 ( 0.0)
C03.0 Gum: Upper 0 ( 0.0) 0 ( 0.0)
C03.1 Gum: Lower 0 ( 0.0) 0 ( 0.0)
C03.9 Gum NOS 0 ( 0.0) 0 ( 0.0)
C04.0 Mouth: Anterior Floor 0 ( 0.0) 0 ( 0.0)
C04.1 Mouth: Lateral Floor 0 ( 0.0) 0 ( 0.0)
C04.9 Floor of Mouth NOS 0 ( 0.0) 0 ( 0.0)
C05.0 Hard Palate 0 ( 0.0) 0 ( 0.0)
C05.1 Soft Palate NOS 0 ( 0.0) 0 ( 0.0)
C05.2 Uvula 0 ( 0.0) 0 ( 0.0)
C05.8 Palate: Overlapping 0 ( 0.0) 0 ( 0.0)
C05.9 Palate NOS 0 ( 0.0) 0 ( 0.0)
C06.0 Cheek Mucosa 0 ( 0.0) 0 ( 0.0)
C06.1 Mouth: Vestibule 0 ( 0.0) 0 ( 0.0)
C06.2 Retromolar Area 0 ( 0.0) 0 ( 0.0)
C06.8 Mouth: Other Overlapping 0 ( 0.0) 0 ( 0.0)
C06.9 Mouth NOS 0 ( 0.0) 0 ( 0.0)
C07.9 Parotid Gland 0 ( 0.0) 0 ( 0.0)
C09.8 Tonsil: Overlapping 0 ( 0.0) 0 ( 0.0)
C09.9 Tonsil NOS 0 ( 0.0) 0 ( 0.0)
C11.1 Nasopharynx: Poster Wall 0 ( 0.0) 0 ( 0.0)
C14.2 Waldeyer Ring 0 ( 0.0) 0 ( 0.0)
C30.0 Nasal Cavity 0 ( 0.0) 0 ( 0.0)
C37.9 Thymus 0 ( 0.0) 0 ( 0.0)
C42.0 Blood 0 ( 0.0) 0 ( 0.0)
C42.2 Spleen 0 ( 0.0) 0 ( 0.0)
C42.4 Hematopoietic NOS 0 ( 0.0) 0 ( 0.0)
C44.0 Skin of lip, NOS 0 ( 0.0) 0 ( 0.0)
C44.1 Eyelid 0 ( 0.0) 0 ( 0.0)
C44.2 External ear 0 ( 0.0) 2 ( 0.2)
C44.3 Skin of ear and unspecified parts of face 0 ( 0.0) 2 ( 0.2)
C44.4 Skin of scalp and neck 0 ( 0.0) 3 ( 0.2)
C44.5 Skin of trunk 10 ( 17.9) 271 ( 21.5)
C44.6 Skin of upper limb and shoulder 1 ( 1.8) 6 ( 0.5)
C44.7 Skin of lower limb and hip 1 ( 1.8) 9 ( 0.7)
C44.8 Overlapping lesion of skin 1 ( 1.8) 9 ( 0.7)
C44.9 Skin, NOS 1 ( 1.8) 16 ( 1.3)
C50.0 Nipple 0 ( 0.0) 0 ( 0.0)
C51.0 Labium majus 3 ( 5.4) 88 ( 7.0)
C51.1 Labium minus 1 ( 1.8) 14 ( 1.1)
C51.2 Clitoris 0 ( 0.0) 2 ( 0.2)
C51.8 Overlapping lesion of vulva 2 ( 3.6) 57 ( 4.5)
C51.9 Vulva, NOS 36 ( 64.3) 757 ( 60.0)
C52.9 Vagina, NOS 0 ( 0.0) 1 ( 0.1)
C60.0 Prepuce 0 ( 0.0) 0 ( 0.0)
C60.1 Glans penis 0 ( 0.0) 0 ( 0.0)
C60.2 Body of penis 0 ( 0.0) 1 ( 0.1)
C60.8 Overlapping lesion of penis 0 ( 0.0) 2 ( 0.2)
C60.9 Penis 0 ( 0.0) 22 ( 1.7)
C63.2 Scrotum, NOS 0 ( 0.0) 0 ( 0.0)
C77.0 Lymph Nodes: HeadFaceNeck 0 ( 0.0) 0 ( 0.0)
C77.1 Intrathoracic Lymph Nodes 0 ( 0.0) 0 ( 0.0)
C77.2 Intra-abdominal LymphNodes 0 ( 0.0) 0 ( 0.0)
C77.3 Lymph Nodes of axilla or arm 0 ( 0.0) 0 ( 0.0)
C77.4 Lymph Nodes: Leg 0 ( 0.0) 0 ( 0.0)
C77.5 Pelvic Lymph Nodes 0 ( 0.0) 0 ( 0.0)
C77.8 Lymph Nodes: multiple region 0 ( 0.0) 0 ( 0.0)
C77.9 Lymph Node NOS 0 ( 0.0) 0 ( 0.0)
BEHAVIOR (%) 2 0 ( 0.0) 0 ( 0.0) NaN
3 56 (100.0) 1262 (100.0)
GRADE_F (%) Gr I: Well Diff 0 ( 0.0) 37 ( 2.9) NaN
Gr II: Mod Diff 1 ( 1.8) 28 ( 2.2)
Gr III: Poor Diff 0 ( 0.0) 36 ( 2.9)
Gr IV: Undiff/Anaplastic 0 ( 0.0) 0 ( 0.0)
5 0 ( 0.0) 0 ( 0.0)
6 0 ( 0.0) 0 ( 0.0)
7 0 ( 0.0) 0 ( 0.0)
8 0 ( 0.0) 0 ( 0.0)
NA/Unkown 55 ( 98.2) 1161 ( 92.0)
DX_STAGING_PROC_DAYS (mean (sd)) 4.42 (27.60) 1.76 (12.91) 0.186
TNM_CLIN_T (%) N_A 0 ( 0.0) 0 ( 0.0) NaN
c0 0 ( 0.0) 6 ( 0.5)
c1 3 ( 5.4) 134 ( 10.6)
c1A 2 ( 3.6) 143 ( 11.3)
c1B 3 ( 5.4) 130 ( 10.3)
c1C 0 ( 0.0) 0 ( 0.0)
c1MI 0 ( 0.0) 0 ( 0.0)
c2 8 ( 14.3) 162 ( 12.8)
c2A 0 ( 0.0) 0 ( 0.0)
c2B 0 ( 0.0) 0 ( 0.0)
c2C 0 ( 0.0) 0 ( 0.0)
c2D 0 ( 0.0) 0 ( 0.0)
c3 2 ( 3.6) 21 ( 1.7)
c3A 0 ( 0.0) 0 ( 0.0)
c3B 0 ( 0.0) 0 ( 0.0)
c4 0 ( 0.0) 1 ( 0.1)
c4A 0 ( 0.0) 0 ( 0.0)
c4B 0 ( 0.0) 0 ( 0.0)
c4C 0 ( 0.0) 0 ( 0.0)
c4D 0 ( 0.0) 0 ( 0.0)
cX 34 ( 60.7) 573 ( 45.4)
pA 0 ( 0.0) 0 ( 0.0)
pIS 0 ( 0.0) 28 ( 2.2)
NA 4 ( 7.1) 64 ( 5.1)
TNM_CLIN_N (%) N_A 0 ( 0.0) 0 ( 0.0) NaN
c0 26 ( 46.4) 825 ( 65.4)
c1 0 ( 0.0) 4 ( 0.3)
c1A 0 ( 0.0) 0 ( 0.0)
c1B 0 ( 0.0) 0 ( 0.0)
c2 0 ( 0.0) 4 ( 0.3)
c2A 0 ( 0.0) 0 ( 0.0)
c2B 0 ( 0.0) 3 ( 0.2)
c2C 0 ( 0.0) 1 ( 0.1)
c3 0 ( 0.0) 0 ( 0.0)
c3A 0 ( 0.0) 0 ( 0.0)
c3B 0 ( 0.0) 0 ( 0.0)
c3C 0 ( 0.0) 0 ( 0.0)
c4 0 ( 0.0) 0 ( 0.0)
cX 27 ( 48.2) 370 ( 29.3)
NA 3 ( 5.4) 55 ( 4.4)
TNM_CLIN_M (%) N_A 0 ( 0.0) 0 ( 0.0) NaN
c0 50 ( 89.3) 1162 ( 92.1)
c0I+ 0 ( 0.0) 0 ( 0.0)
c1 1 ( 1.8) 10 ( 0.8)
c1A 0 ( 0.0) 0 ( 0.0)
c1B 0 ( 0.0) 0 ( 0.0)
c1C 0 ( 0.0) 0 ( 0.0)
NA 5 ( 8.9) 90 ( 7.1)
TNM_CLIN_STAGE_GROUP (%) 0 1 ( 1.8) 61 ( 4.8) NaN
1 3 ( 5.4) 189 ( 15.0)
1A 1 ( 1.8) 106 ( 8.4)
1B 2 ( 3.6) 113 ( 9.0)
1C 0 ( 0.0) 0 ( 0.0)
2 7 ( 12.5) 170 ( 13.5)
2A 0 ( 0.0) 0 ( 0.0)
2B 0 ( 0.0) 0 ( 0.0)
2C 0 ( 0.0) 0 ( 0.0)
3 1 ( 1.8) 14 ( 1.1)
3A 0 ( 0.0) 0 ( 0.0)
3B 0 ( 0.0) 1 ( 0.1)
3C 0 ( 0.0) 0 ( 0.0)
4 1 ( 1.8) 8 ( 0.6)
4A 0 ( 0.0) 2 ( 0.2)
4A1 0 ( 0.0) 0 ( 0.0)
4A2 0 ( 0.0) 0 ( 0.0)
4B 0 ( 0.0) 5 ( 0.4)
4C 0 ( 0.0) 0 ( 0.0)
N_A 0 ( 0.0) 0 ( 0.0)
99 40 ( 71.4) 593 ( 47.0)
TNM_PATH_T (%) N_A 0 ( 0.0) 0 ( 0.0) NaN
p0 0 ( 0.0) 9 ( 0.7)
p1 1 ( 1.8) 94 ( 7.4)
p1A 0 ( 0.0) 144 ( 11.4)
p1B 0 ( 0.0) 152 ( 12.0)
p1C 0 ( 0.0) 0 ( 0.0)
p1MI 0 ( 0.0) 0 ( 0.0)
p2 0 ( 0.0) 140 ( 11.1)
p2A 0 ( 0.0) 0 ( 0.0)
p2B 0 ( 0.0) 0 ( 0.0)
p2C 0 ( 0.0) 0 ( 0.0)
p2D 0 ( 0.0) 0 ( 0.0)
p3 0 ( 0.0) 22 ( 1.7)
p3A 0 ( 0.0) 0 ( 0.0)
p3B 0 ( 0.0) 0 ( 0.0)
p4 0 ( 0.0) 1 ( 0.1)
p4A 0 ( 0.0) 0 ( 0.0)
p4B 0 ( 0.0) 0 ( 0.0)
p4C 0 ( 0.0) 0 ( 0.0)
p4D 0 ( 0.0) 0 ( 0.0)
pA 0 ( 0.0) 0 ( 0.0)
pIS 0 ( 0.0) 23 ( 1.8)
pX 37 ( 66.1) 542 ( 42.9)
NA 18 ( 32.1) 135 ( 10.7)
TNM_PATH_N (%) N_A 0 ( 0.0) 0 ( 0.0) NaN
p0 0 ( 0.0) 272 ( 21.6)
p0I- 0 ( 0.0) 0 ( 0.0)
p0I+ 0 ( 0.0) 0 ( 0.0)
p0M- 0 ( 0.0) 0 ( 0.0)
p0M+ 0 ( 0.0) 0 ( 0.0)
p1 0 ( 0.0) 9 ( 0.7)
p1A 0 ( 0.0) 1 ( 0.1)
p1B 0 ( 0.0) 1 ( 0.1)
p1C 0 ( 0.0) 0 ( 0.0)
p1MI 0 ( 0.0) 0 ( 0.0)
p2 0 ( 0.0) 1 ( 0.1)
p2A 0 ( 0.0) 0 ( 0.0)
p2B 0 ( 0.0) 1 ( 0.1)
p2C 0 ( 0.0) 5 ( 0.4)
p3 0 ( 0.0) 1 ( 0.1)
p3A 0 ( 0.0) 0 ( 0.0)
p3B 0 ( 0.0) 0 ( 0.0)
p3C 0 ( 0.0) 0 ( 0.0)
p4 0 ( 0.0) 0 ( 0.0)
pX 38 ( 67.9) 781 ( 61.9)
NA 18 ( 32.1) 190 ( 15.1)
TNM_PATH_M (%) N_A 0 ( 0.0) 0 ( 0.0) NaN
p0 0 ( 0.0) 0 ( 0.0)
p1 0 ( 0.0) 3 ( 0.2)
p1A 0 ( 0.0) 0 ( 0.0)
p1B 0 ( 0.0) 0 ( 0.0)
p1C 0 ( 0.0) 0 ( 0.0)
pX 25 ( 44.6) 577 ( 45.7)
NA 31 ( 55.4) 682 ( 54.0)
TNM_PATH_STAGE_GROUP (%) 0 0 ( 0.0) 52 ( 4.1) NaN
1 0 ( 0.0) 102 ( 8.1)
1A 0 ( 0.0) 103 ( 8.2)
1B 0 ( 0.0) 91 ( 7.2)
1C 0 ( 0.0) 0 ( 0.0)
2 0 ( 0.0) 115 ( 9.1)
2A 0 ( 0.0) 0 ( 0.0)
2B 0 ( 0.0) 0 ( 0.0)
2C 0 ( 0.0) 0 ( 0.0)
3 0 ( 0.0) 26 ( 2.1)
3A 0 ( 0.0) 2 ( 0.2)
3B 0 ( 0.0) 0 ( 0.0)
3C 0 ( 0.0) 4 ( 0.3)
4 0 ( 0.0) 4 ( 0.3)
4A 0 ( 0.0) 5 ( 0.4)
4A1 0 ( 0.0) 0 ( 0.0)
4B 0 ( 0.0) 1 ( 0.1)
4C 0 ( 0.0) 0 ( 0.0)
N_A 0 ( 0.0) 0 ( 0.0)
99 44 ( 78.6) 691 ( 54.8)
NA 12 ( 21.4) 66 ( 5.2)
DX_RX_STARTED_DAYS (mean (sd)) NaN (NA) 49.81 (135.39) NA
DX_SURG_STARTED_DAYS (mean (sd)) NaN (NA) 47.17 (120.64) NA
DX_DEFSURG_STARTED_DAYS (mean (sd)) NaN (NA) 59.00 (135.98) NA
MARGINS (%) No Residual 0 ( 0.0) 583 ( 46.2) <0.001
Residual, NOS 0 ( 0.0) 160 ( 12.7)
Microscopic Resid 0 ( 0.0) 338 ( 26.8)
Macroscopic Resid 0 ( 0.0) 17 ( 1.3)
Not evaluable 0 ( 0.0) 21 ( 1.7)
No surg 56 (100.0) 102 ( 8.1)
Unknown 0 ( 0.0) 41 ( 3.2)
MARGINS_YN (%) No 0 ( 0.0) 583 ( 46.2) <0.001
Yes 0 ( 0.0) 515 ( 40.8)
No surg/Unk/NA 56 (100.0) 164 ( 13.0)
SURG_DISCHARGE_DAYS (mean (sd)) NaN (NA) 2.15 (7.04) NA
READM_HOSP_30_DAYS_F (%) No_Surg_or_No_Readmit 55 ( 98.2) 1199 ( 95.0) 0.821
Unplan_Readmit_Same 1 ( 1.8) 33 ( 2.6)
Plan_Readmit_Same 0 ( 0.0) 15 ( 1.2)
PlanUnplan_Same 0 ( 0.0) 1 ( 0.1)
9 0 ( 0.0) 14 ( 1.1)
RX_SUMM_RADIATION_F (%) None 56 (100.0) 1180 ( 93.5) NaN
Beam Radiation 0 ( 0.0) 73 ( 5.8)
Radioactive Implants 0 ( 0.0) 1 ( 0.1)
Radioisotopes 0 ( 0.0) 0 ( 0.0)
Beam + Imp or Isotopes 0 ( 0.0) 1 ( 0.1)
Radiation, NOS 0 ( 0.0) 0 ( 0.0)
Unknown 0 ( 0.0) 7 ( 0.6)
PUF_30_DAY_MORT_CD_F (%) Alive_30 0 ( 0.0) 1118 ( 88.6) <0.001
Dead_30 0 ( 0.0) 5 ( 0.4)
Unknown 0 ( 0.0) 33 ( 2.6)
NA 56 (100.0) 106 ( 8.4)
PUF_90_DAY_MORT_CD_F (%) Alive_90 0 ( 0.0) 1096 ( 86.8) <0.001
Dead_90 0 ( 0.0) 10 ( 0.8)
Unknown 0 ( 0.0) 50 ( 4.0)
NA 56 (100.0) 106 ( 8.4)
DX_LASTCONTACT_DEATH_MONTHS (mean (sd)) 38.26 (34.59) 58.37 (40.04) <0.001
LYMPH_VASCULAR_INVASION_F (%) Neg_LymphVasc_Inv 6 ( 10.7) 257 ( 20.4) 0.167
Pos_LumphVasc_Inv 0 ( 0.0) 26 ( 2.1)
N_A 0 ( 0.0) 1 ( 0.1)
Unknown 25 ( 44.6) 401 ( 31.8)
NA 25 ( 44.6) 577 ( 45.7)
RX_HOSP_SURG_APPR_2010_F (%) No_Surg 31 ( 55.4) 89 ( 7.1) NaN
Robot_Assist 0 ( 0.0) 2 ( 0.2)
Robot_to_Open 0 ( 0.0) 0 ( 0.0)
Endo_Lap 0 ( 0.0) 18 ( 1.4)
Endo_Lap_to_Open 0 ( 0.0) 1 ( 0.1)
Open_Unknown 0 ( 0.0) 575 ( 45.6)
Unknown 0 ( 0.0) 0 ( 0.0)
NA 25 ( 44.6) 577 ( 45.7)
SURG_RAD_SEQ (%) Surg Alone 0 ( 0.0) 1129 ( 89.5) NaN
Surg then Rad 0 ( 0.0) 23 ( 1.8)
Rad Alone 0 ( 0.0) 52 ( 4.1)
No Treatment 56 (100.0) 47 ( 3.7)
Other 0 ( 0.0) 11 ( 0.9)
Rad before and after Surg 0 ( 0.0) 0 ( 0.0)
Rad then Surg 0 ( 0.0) 0 ( 0.0)
SURG_RAD_SEQ_C (%) Surg, No rad, No Chemo 0 ( 0.0) 1119 ( 88.7) NaN
Surg then Rad, No Chemo 0 ( 0.0) 20 ( 1.6)
Surg then Rad, Yes Chemo 0 ( 0.0) 3 ( 0.2)
Surg, No rad, Yes Chemo 0 ( 0.0) 10 ( 0.8)
No Surg, No Rad, Yes Chemo 0 ( 0.0) 9 ( 0.7)
No Surg, No Rad, No Chemo 56 (100.0) 38 ( 3.0)
Other 0 ( 0.0) 11 ( 0.9)
Rad, No Surg, Yes Chemo 0 ( 0.0) 9 ( 0.7)
Rad, No Surg, No Chemo 0 ( 0.0) 43 ( 3.4)
Rad then Surg, Yes Chemo 0 ( 0.0) 0 ( 0.0)
Rad then Surg, No Chemo 0 ( 0.0) 0 ( 0.0)
Rad before and after Surg, Yes Chemo 0 ( 0.0) 0 ( 0.0)
Rad before and after Surg, No Chemo 0 ( 0.0) 0 ( 0.0)
T_SIZE (%) No Tumor 0 ( 0.0) 1 ( 0.1) 0.001
Microscopic focus 0 ( 0.0) 24 ( 1.9)
< 1 cm 1 ( 1.8) 94 ( 7.4)
1-2 cm 1 ( 1.8) 95 ( 7.5)
2-3 cm 0 ( 0.0) 93 ( 7.4)
3-4 cm 2 ( 3.6) 109 ( 8.6)
4-5 cm 2 ( 3.6) 76 ( 6.0)
5-6 cm 2 ( 3.6) 70 ( 5.5)
>6 cm 5 ( 8.9) 160 ( 12.7)
NA_unk 43 ( 76.8) 540 ( 42.8)
SURGERY_YN (%) No 56 (100.0) 98 ( 7.8) <0.001
Ukn 0 ( 0.0) 6 ( 0.5)
Yes 0 ( 0.0) 1158 ( 91.8)
RADIATION_YN (%) No 56 (100.0) 1180 ( 93.5) 0.144
Yes 0 ( 0.0) 75 ( 5.9)
NA 0 ( 0.0) 7 ( 0.6)
CHEMO_YN (%) No 56 (100.0) 1231 ( 97.5) NaN
Yes 0 ( 0.0) 31 ( 2.5)
Ukn 0 ( 0.0) 0 ( 0.0)
mets_at_dx (%) Bone 0 ( 0.0) 5 ( 0.4) NaN
Brain 0 ( 0.0) 0 ( 0.0)
Liver 0 ( 0.0) 0 ( 0.0)
Lung 0 ( 0.0) 0 ( 0.0)
None/Other/Unk/NA 56 (100.0) 1257 ( 99.6)
IMMUNO_YN (%) No 56 (100.0) 1192 ( 94.5) 0.194
Yes 0 ( 0.0) 69 ( 5.5)
Ukn 0 ( 0.0) 1 ( 0.1)
Tx_YN (%) FALSE 56 (100.0) 0 ( 0.0) NaN
TRUE 0 ( 0.0) 1262 (100.0)
NA 0 ( 0.0) 0 ( 0.0)
MEDICAID_EXPN_CODE (%) Non-Expansion State 18 ( 32.1) 449 ( 35.6) 0.435
Jan 2014 Expansion States 21 ( 37.5) 415 ( 32.9)
Early Expansion States (2010-13) 13 ( 23.2) 217 ( 17.2)
Late Expansion States (> Jan 2014) 4 ( 7.1) 167 ( 13.2)
Suppressed for Ages 0 - 39 0 ( 0.0) 14 ( 1.1)

Kaplan Meier Analysis

All

uni_var(test_var = "All", data_imp = data)
_________________________________________________
   
## All
_________________________________________________
Call: survfit(formula = Surv(DX_LASTCONTACT_DEATH_MONTHS, PUF_VITAL_STATUS == 
    0) ~ All, data = data)

      n  events  median 0.95LCL 0.95UCL 
   1360     329     142     128      NA 

Call: survfit(formula = Surv(DX_LASTCONTACT_DEATH_MONTHS, PUF_VITAL_STATUS == 
    0) ~ All, data = data)

 time n.risk n.event survival std.err lower 95% CI upper 95% CI
   12   1184      48    0.963 0.00528        0.952        0.973
   24   1032      52    0.919 0.00783        0.903        0.934
   36    864      40    0.880 0.00957        0.862        0.899
   48    724      34    0.843 0.01107        0.822        0.865
   60    592      44    0.788 0.01309        0.763        0.815
  120    125      95    0.586 0.02187        0.545        0.631



   
## Univariable Cox Proportional Hazard Model for:  All

[1] "Only one level, no Cox model performed"




   
## Unadjusted Kaplan Meier Overall Survival Curve for:  All

Facility Type

uni_var(test_var = "FACILITY_TYPE_F", data_imp = data)
_________________________________________________
   
## FACILITY_TYPE_F
_________________________________________________
Call: survfit(formula = Surv(DX_LASTCONTACT_DEATH_MONTHS, PUF_VITAL_STATUS == 
    0) ~ FACILITY_TYPE_F, data = data)

   14 observations deleted due to missingness 
                                                n events median 0.95LCL 0.95UCL
FACILITY_TYPE_F=Community Cancer Program       31      8     NA    79.8      NA
FACILITY_TYPE_F=Comprehensive Comm Ca Program 462    126    128   115.9      NA
FACILITY_TYPE_F=Academic/Research Program     654    144    155   126.1      NA
FACILITY_TYPE_F=Integrated Network Ca Program 199     51    131   113.8      NA

Call: survfit(formula = Surv(DX_LASTCONTACT_DEATH_MONTHS, PUF_VITAL_STATUS == 
    0) ~ FACILITY_TYPE_F, data = data)

14 observations deleted due to missingness 
                FACILITY_TYPE_F=Community Cancer Program 
 time n.risk n.event survival std.err lower 95% CI upper 95% CI
   12     26       1    0.963  0.0363        0.894        1.000
   24     23       1    0.926  0.0504        0.832        1.000
   36     19       1    0.882  0.0645        0.764        1.000
   48     15       0    0.882  0.0645        0.764        1.000
   60     14       1    0.823  0.0827        0.676        1.000
  120      5       4    0.525  0.1366        0.316        0.875

                FACILITY_TYPE_F=Comprehensive Comm Ca Program 
 time n.risk n.event survival std.err lower 95% CI upper 95% CI
   12    393      24    0.946  0.0108        0.925        0.967
   24    345      18    0.901  0.0145        0.873        0.930
   36    294      14    0.862  0.0172        0.829        0.897
   48    250      17    0.811  0.0203        0.772        0.851
   60    197      16    0.753  0.0234        0.708        0.800
  120     34      31    0.555  0.0384        0.484        0.635

                FACILITY_TYPE_F=Academic/Research Program 
 time n.risk n.event survival std.err lower 95% CI upper 95% CI
   12    581      15    0.976 0.00624        0.963        0.988
   24    505      24    0.933 0.01037        0.913        0.954
   36    417      20    0.893 0.01323        0.868        0.919
   48    352      10    0.870 0.01476        0.842        0.900
   60    294      20    0.818 0.01792        0.784        0.854
  120     64      48    0.597 0.03241        0.537        0.664

                FACILITY_TYPE_F=Integrated Network Ca Program 
 time n.risk n.event survival std.err lower 95% CI upper 95% CI
   12    173       8    0.957  0.0147        0.929        0.987
   24    148       9    0.904  0.0221        0.862        0.949
   36    126       5    0.871  0.0258        0.822        0.923
   48    100       7    0.818  0.0311        0.760        0.882
   60     80       7    0.758  0.0362        0.691        0.833
  120     19      12    0.596  0.0533        0.500        0.710




   
## Univariable Cox Proportional Hazard Model for:  FACILITY_TYPE_F

Call:
coxph(formula = Surv(DX_LASTCONTACT_DEATH_MONTHS, PUF_VITAL_STATUS == 
    0) ~ FACILITY_TYPE_F, data = data)

  n= 1346, number of events= 329 
   (14 observations deleted due to missingness)

                                                 coef exp(coef) se(coef)      z Pr(>|z|)
FACILITY_TYPE_FComprehensive Comm Ca Program  0.09863   1.10366  0.36480  0.270    0.787
FACILITY_TYPE_FAcademic/Research Program     -0.16094   0.85135  0.36339 -0.443    0.658
FACILITY_TYPE_FIntegrated Network Ca Program  0.03928   1.04006  0.38045  0.103    0.918

                                             exp(coef) exp(-coef) lower .95 upper .95
FACILITY_TYPE_FComprehensive Comm Ca Program    1.1037     0.9061    0.5399     2.256
FACILITY_TYPE_FAcademic/Research Program        0.8513     1.1746    0.4176     1.735
FACILITY_TYPE_FIntegrated Network Ca Program    1.0401     0.9615    0.4934     2.192

Concordance= 0.543  (se = 0.016 )
Rsquare= 0.004   (max possible= 0.957 )
Likelihood ratio test= 4.81  on 3 df,   p=0.1859
Wald test            = 4.8  on 3 df,   p=0.1873
Score (logrank) test = 4.82  on 3 df,   p=0.1854
Removed 1 rows containing missing values (geom_errorbar).



   
## Unadjusted Kaplan Meier Overall Survival Curve for:  FACILITY_TYPE_F

Facility Location

uni_var(test_var = "FACILITY_LOCATION_F", data_imp = data)
_________________________________________________
   
## FACILITY_LOCATION_F
_________________________________________________
Call: survfit(formula = Surv(DX_LASTCONTACT_DEATH_MONTHS, PUF_VITAL_STATUS == 
    0) ~ FACILITY_LOCATION_F, data = data)

   14 observations deleted due to missingness 
                                         n events median 0.95LCL 0.95UCL
FACILITY_LOCATION_F=New England         59     18    111    70.2      NA
FACILITY_LOCATION_F=Middle Atlantic    231     52     NA   115.0      NA
FACILITY_LOCATION_F=South Atlantic     255     51     NA   126.1      NA
FACILITY_LOCATION_F=East North Central 222     60    133   105.8      NA
FACILITY_LOCATION_F=East South Central  89     20    133   110.7      NA
FACILITY_LOCATION_F=West North Central 141     38    125    84.9      NA
FACILITY_LOCATION_F=West South Central  99     26     NA   125.6      NA
FACILITY_LOCATION_F=Mountain            79     25    128   106.7      NA
FACILITY_LOCATION_F=Pacific            171     39    150   149.6      NA

Call: survfit(formula = Surv(DX_LASTCONTACT_DEATH_MONTHS, PUF_VITAL_STATUS == 
    0) ~ FACILITY_LOCATION_F, data = data)

14 observations deleted due to missingness 
                FACILITY_LOCATION_F=New England 
 time n.risk n.event survival std.err lower 95% CI upper 95% CI
   12     52       2    0.964  0.0253        0.915        1.000
   24     43       3    0.905  0.0404        0.829        0.988
   36     37       0    0.905  0.0404        0.829        0.988
   48     30       3    0.823  0.0583        0.716        0.946
   60     21       4    0.700  0.0756        0.567        0.865
  120      4       5    0.429  0.1213        0.247        0.747

                FACILITY_LOCATION_F=Middle Atlantic 
 time n.risk n.event survival std.err lower 95% CI upper 95% CI
   12    213       4    0.982 0.00896        0.965        1.000
   24    180      12    0.923 0.01846        0.888        0.960
   36    146       6    0.890 0.02227        0.847        0.935
   48    118       8    0.837 0.02768        0.785        0.893
   60     98       6    0.793 0.03150        0.734        0.858
  120     19      16    0.577 0.05660        0.476        0.700

                FACILITY_LOCATION_F=South Atlantic 
 time n.risk n.event survival std.err lower 95% CI upper 95% CI
   12    224       7    0.971  0.0108        0.950        0.992
   24    207       3    0.957  0.0132        0.932        0.984
   36    178       8    0.918  0.0186        0.883        0.955
   48    152       6    0.886  0.0221        0.843        0.930
   60    128       8    0.836  0.0271        0.784        0.890
  120     28      16    0.651  0.0493        0.561        0.755

                FACILITY_LOCATION_F=East North Central 
 time n.risk n.event survival std.err lower 95% CI upper 95% CI
   12    198      10    0.954  0.0143        0.926        0.982
   24    178       6    0.924  0.0183        0.889        0.960
   36    151       7    0.884  0.0229        0.840        0.930
   48    125       5    0.852  0.0261        0.803        0.905
   60    105       8    0.795  0.0313        0.736        0.859
  120     18      22    0.537  0.0545        0.440        0.655

                FACILITY_LOCATION_F=East South Central 
 time n.risk n.event survival std.err lower 95% CI upper 95% CI
   12     74       2    0.976  0.0165        0.945        1.000
   24     67       3    0.935  0.0283        0.881        0.992
   36     54       4    0.872  0.0401        0.797        0.955
   48     48       1    0.856  0.0427        0.776        0.944
   60     36       3    0.795  0.0521        0.700        0.904
  120      7       6    0.604  0.0826        0.461        0.789

                FACILITY_LOCATION_F=West North Central 
 time n.risk n.event survival std.err lower 95% CI upper 95% CI
   12    119       4    0.970  0.0150        0.941        0.999
   24     98       9    0.892  0.0284        0.838        0.949
   36     80       3    0.861  0.0325        0.800        0.927
   48     59       7    0.781  0.0413        0.704        0.867
   60     47       2    0.753  0.0444        0.671        0.845
  120     11       9    0.536  0.0719        0.412        0.697

                FACILITY_LOCATION_F=West South Central 
 time n.risk n.event survival std.err lower 95% CI upper 95% CI
   12     81       5    0.945  0.0241        0.899        0.993
   24     73       4    0.897  0.0327        0.835        0.963
   36     66       3    0.860  0.0377        0.789        0.937
   48     59       2    0.833  0.0411        0.756        0.917
   60     46       5    0.757  0.0494        0.666        0.860
  120      8       6    0.622  0.0662        0.505        0.767

                FACILITY_LOCATION_F=Mountain 
 time n.risk n.event survival std.err lower 95% CI upper 95% CI
   12     70       4    0.946  0.0261        0.896        0.999
   24     60       3    0.904  0.0346        0.839        0.974
   36     51       3    0.856  0.0425        0.776        0.943
   48     44       2    0.820  0.0476        0.732        0.919
   60     38       3    0.761  0.0550        0.661        0.877
  120     12       8    0.533  0.0805        0.397        0.717

                FACILITY_LOCATION_F=Pacific 
 time n.risk n.event survival std.err lower 95% CI upper 95% CI
   12    142      10    0.939  0.0188        0.903        0.976
   24    115       9    0.876  0.0267        0.825        0.930
   36     93       6    0.828  0.0318        0.768        0.892
   48     82       0    0.828  0.0318        0.768        0.892
   60     66       5    0.772  0.0382        0.700        0.850
  120     15       7    0.640  0.0577        0.536        0.763




   
## Univariable Cox Proportional Hazard Model for:  FACILITY_LOCATION_F

Call:
coxph(formula = Surv(DX_LASTCONTACT_DEATH_MONTHS, PUF_VITAL_STATUS == 
    0) ~ FACILITY_LOCATION_F, data = data)

  n= 1346, number of events= 329 
   (14 observations deleted due to missingness)

                                         coef exp(coef) se(coef)      z Pr(>|z|)  
FACILITY_LOCATION_FMiddle Atlantic    -0.4202    0.6569   0.2737 -1.536   0.1247  
FACILITY_LOCATION_FSouth Atlantic     -0.6586    0.5176   0.2745 -2.400   0.0164 *
FACILITY_LOCATION_FEast North Central -0.3203    0.7259   0.2690 -1.191   0.2337  
FACILITY_LOCATION_FEast South Central -0.4575    0.6329   0.3252 -1.407   0.1594  
FACILITY_LOCATION_FWest North Central -0.0866    0.9170   0.2862 -0.303   0.7622  
FACILITY_LOCATION_FWest South Central -0.3416    0.7106   0.3069 -1.113   0.2657  
FACILITY_LOCATION_FMountain           -0.2283    0.7959   0.3097 -0.737   0.4610  
FACILITY_LOCATION_FPacific            -0.3479    0.7062   0.2852 -1.220   0.2226  
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

                                      exp(coef) exp(-coef) lower .95 upper .95
FACILITY_LOCATION_FMiddle Atlantic       0.6569      1.522    0.3842    1.1232
FACILITY_LOCATION_FSouth Atlantic        0.5176      1.932    0.3023    0.8863
FACILITY_LOCATION_FEast North Central    0.7259      1.378    0.4285    1.2298
FACILITY_LOCATION_FEast South Central    0.6329      1.580    0.3346    1.1970
FACILITY_LOCATION_FWest North Central    0.9170      1.090    0.5233    1.6069
FACILITY_LOCATION_FWest South Central    0.7106      1.407    0.3894    1.2969
FACILITY_LOCATION_FMountain              0.7959      1.256    0.4338    1.4603
FACILITY_LOCATION_FPacific               0.7062      1.416    0.4038    1.2351

Concordance= 0.547  (se = 0.018 )
Rsquare= 0.008   (max possible= 0.957 )
Likelihood ratio test= 10.59  on 8 df,   p=0.2262
Wald test            = 10.59  on 8 df,   p=0.2263
Score (logrank) test = 10.75  on 8 df,   p=0.2164
Removed 1 rows containing missing values (geom_errorbar).



   
## Unadjusted Kaplan Meier Overall Survival Curve for:  FACILITY_LOCATION_F

Facility Geography

uni_var(test_var = "FACILITY_GEOGRAPHY", data_imp = data)
_________________________________________________
   
## FACILITY_GEOGRAPHY
_________________________________________________
Call: survfit(formula = Surv(DX_LASTCONTACT_DEATH_MONTHS, PUF_VITAL_STATUS == 
    0) ~ FACILITY_GEOGRAPHY, data = data)

   14 observations deleted due to missingness 
                               n events median 0.95LCL 0.95UCL
FACILITY_GEOGRAPHY=Northeast 290     70     NA     111      NA
FACILITY_GEOGRAPHY=South     354     77     NA     126      NA
FACILITY_GEOGRAPHY=Midwest   452    118    126     111      NA
FACILITY_GEOGRAPHY=West      250     64    150     128      NA

Call: survfit(formula = Surv(DX_LASTCONTACT_DEATH_MONTHS, PUF_VITAL_STATUS == 
    0) ~ FACILITY_GEOGRAPHY, data = data)

14 observations deleted due to missingness 
                FACILITY_GEOGRAPHY=Northeast 
 time n.risk n.event survival std.err lower 95% CI upper 95% CI
   12    265       6    0.978 0.00875        0.961        0.996
   24    223      15    0.920 0.01682        0.887        0.953
   36    183       6    0.893 0.01957        0.856        0.932
   48    148      11    0.835 0.02496        0.787        0.885
   60    119      10    0.776 0.02940        0.720        0.836
  120     23      21    0.549 0.05180        0.456        0.660

                FACILITY_GEOGRAPHY=South 
 time n.risk n.event survival std.err lower 95% CI upper 95% CI
   12    305      12    0.964  0.0103        0.944        0.984
   24    280       7    0.941  0.0132        0.915        0.967
   36    244      11    0.902  0.0170        0.869        0.936
   48    211       8    0.871  0.0197        0.834        0.911
   60    174      13    0.813  0.0240        0.768        0.862
  120     36      22    0.640  0.0407        0.565        0.725

                FACILITY_GEOGRAPHY=Midwest 
 time n.risk n.event survival std.err lower 95% CI upper 95% CI
   12    391      16    0.963  0.0091        0.945        0.981
   24    343      18    0.917  0.0137        0.890        0.944
   36    285      14    0.875  0.0170        0.843        0.909
   48    232      13    0.833  0.0198        0.795        0.873
   60    188      13    0.783  0.0230        0.739        0.829
  120     36      37    0.549  0.0387        0.478        0.630

                FACILITY_GEOGRAPHY=West 
 time n.risk n.event survival std.err lower 95% CI upper 95% CI
   12    212      14    0.941  0.0153        0.912        0.972
   24    175      12    0.885  0.0212        0.845        0.928
   36    144       9    0.837  0.0255        0.788        0.888
   48    126       2    0.824  0.0267        0.773        0.878
   60    104       8    0.767  0.0315        0.708        0.832
  120     27      15    0.591  0.0493        0.502        0.696




   
## Univariable Cox Proportional Hazard Model for:  FACILITY_GEOGRAPHY

Call:
coxph(formula = Surv(DX_LASTCONTACT_DEATH_MONTHS, PUF_VITAL_STATUS == 
    0) ~ FACILITY_GEOGRAPHY, data = data)

  n= 1346, number of events= 329 
   (14 observations deleted due to missingness)

                              coef exp(coef) se(coef)      z Pr(>|z|)
FACILITY_GEOGRAPHYSouth   -0.23411   0.79128  0.16534 -1.416    0.157
FACILITY_GEOGRAPHYMidwest  0.05075   1.05206  0.15096  0.336    0.737
FACILITY_GEOGRAPHYWest     0.02548   1.02581  0.17315  0.147    0.883

                          exp(coef) exp(-coef) lower .95 upper .95
FACILITY_GEOGRAPHYSouth      0.7913     1.2638    0.5723     1.094
FACILITY_GEOGRAPHYMidwest    1.0521     0.9505    0.7826     1.414
FACILITY_GEOGRAPHYWest       1.0258     0.9748    0.7306     1.440

Concordance= 0.526  (se = 0.017 )
Rsquare= 0.003   (max possible= 0.957 )
Likelihood ratio test= 4.41  on 3 df,   p=0.2201
Wald test            = 4.23  on 3 df,   p=0.2375
Score (logrank) test = 4.26  on 3 df,   p=0.2351
Removed 1 rows containing missing values (geom_errorbar).



   
## Unadjusted Kaplan Meier Overall Survival Curve for:  FACILITY_GEOGRAPHY

Age Group

uni_var(test_var = "AGE_F", data_imp = data)
_________________________________________________
   
## AGE_F
_________________________________________________
Call: survfit(formula = Surv(DX_LASTCONTACT_DEATH_MONTHS, PUF_VITAL_STATUS == 
    0) ~ AGE_F, data = data)

                 n events median 0.95LCL 0.95UCL
AGE_F=(0,54]   144     10     NA      NA      NA
AGE_F=(54,64]  254     25     NA     155      NA
AGE_F=(64,74]  421     68  149.6     132      NA
AGE_F=(74,100] 541    226   81.8      73    95.5

Call: survfit(formula = Surv(DX_LASTCONTACT_DEATH_MONTHS, PUF_VITAL_STATUS == 
    0) ~ AGE_F, data = data)

                AGE_F=(0,54] 
 time n.risk n.event survival std.err lower 95% CI upper 95% CI
   12    127       1    0.993 0.00697        0.979        1.000
   24    119       1    0.985 0.01060        0.964        1.000
   36    106       1    0.976 0.01358        0.950        1.000
   48     93       0    0.976 0.01358        0.950        1.000
   60     76       4    0.930 0.02586        0.881        0.983
  120     24       2    0.897 0.03449        0.832        0.967

                AGE_F=(54,64] 
 time n.risk n.event survival std.err lower 95% CI upper 95% CI
   12    227       3    0.988 0.00695        0.974        1.000
   24    209       6    0.961 0.01288        0.936        0.986
   36    176       2    0.950 0.01475        0.922        0.979
   48    151       3    0.933 0.01755        0.899        0.968
   60    126       2    0.920 0.01961        0.882        0.959
  120     37       6    0.863 0.02906        0.808        0.922

                AGE_F=(64,74] 
 time n.risk n.event survival std.err lower 95% CI upper 95% CI
   12    370       7    0.983 0.00653        0.970        0.995
   24    319      11    0.952 0.01111        0.930        0.974
   36    265      13    0.911 0.01531        0.882        0.942
   48    228       2    0.904 0.01601        0.873        0.936
   60    190       8    0.871 0.01924        0.834        0.910
  120     37      22    0.684 0.04208        0.606        0.772

                AGE_F=(74,100] 
 time n.risk n.event survival std.err lower 95% CI upper 95% CI
   12    460      37    0.928  0.0115        0.905        0.950
   24    385      34    0.856  0.0159        0.825        0.888
   36    317      24    0.798  0.0187        0.762        0.835
   48    252      29    0.720  0.0218        0.679        0.764
   60    200      30    0.630  0.0245        0.584        0.680
  120     27      65    0.306  0.0347        0.245        0.382




   
## Univariable Cox Proportional Hazard Model for:  AGE_F

Call:
coxph(formula = Surv(DX_LASTCONTACT_DEATH_MONTHS, PUF_VITAL_STATUS == 
    0) ~ AGE_F, data = data)

  n= 1360, number of events= 329 

                coef exp(coef) se(coef)     z Pr(>|z|)    
AGE_F(54,64]  0.4320    1.5404   0.3743 1.154  0.24839    
AGE_F(64,74]  1.0943    2.9870   0.3392 3.226  0.00126 ** 
AGE_F(74,100] 2.2006    9.0307   0.3242 6.787 1.14e-11 ***
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

              exp(coef) exp(-coef) lower .95 upper .95
AGE_F(54,64]      1.540     0.6492    0.7397     3.208
AGE_F(64,74]      2.987     0.3348    1.5363     5.808
AGE_F(74,100]     9.031     0.1107    4.7833    17.050

Concordance= 0.685  (se = 0.017 )
Rsquare= 0.124   (max possible= 0.956 )
Likelihood ratio test= 180.1  on 3 df,   p=0
Wald test            = 147  on 3 df,   p=0
Score (logrank) test = 182.2  on 3 df,   p=0
Removed 1 rows containing missing values (geom_errorbar).



   
## Unadjusted Kaplan Meier Overall Survival Curve for:  AGE_F

Age Group

uni_var(test_var = "AGE_40", data_imp = data)
_________________________________________________
   
## AGE_40
_________________________________________________
Call: survfit(formula = Surv(DX_LASTCONTACT_DEATH_MONTHS, PUF_VITAL_STATUS == 
    0) ~ AGE_40, data = data)

                   n events median 0.95LCL 0.95UCL
AGE_40=(0,40]     18      1     NA      NA      NA
AGE_40=(40,100] 1342    328    142     126      NA

Call: survfit(formula = Surv(DX_LASTCONTACT_DEATH_MONTHS, PUF_VITAL_STATUS == 
    0) ~ AGE_40, data = data)

                AGE_40=(0,40] 
 time n.risk n.event survival std.err lower 95% CI upper 95% CI
   12     14       0    1.000  0.0000        1.000            1
   24     13       1    0.929  0.0688        0.803            1
   36     10       0    0.929  0.0688        0.803            1
   48      9       0    0.929  0.0688        0.803            1
   60      9       0    0.929  0.0688        0.803            1
  120      3       0    0.929  0.0688        0.803            1

                AGE_40=(40,100] 
 time n.risk n.event survival std.err lower 95% CI upper 95% CI
   12   1170      48    0.962 0.00534        0.952        0.973
   24   1019      51    0.918 0.00788        0.903        0.934
   36    854      40    0.880 0.00965        0.861        0.899
   48    715      34    0.842 0.01116        0.821        0.864
   60    583      44    0.787 0.01321        0.761        0.813
  120    122      95    0.581 0.02214        0.540        0.626




   
## Univariable Cox Proportional Hazard Model for:  AGE_40

Call:
coxph(formula = Surv(DX_LASTCONTACT_DEATH_MONTHS, PUF_VITAL_STATUS == 
    0) ~ AGE_40, data = data)

  n= 1360, number of events= 329 

                coef exp(coef) se(coef)     z Pr(>|z|)
AGE_40(40,100] 1.637     5.138    1.002 1.634    0.102

               exp(coef) exp(-coef) lower .95 upper .95
AGE_40(40,100]     5.138     0.1946    0.7212     36.61

Concordance= 0.504  (se = 0.003 )
Rsquare= 0.004   (max possible= 0.956 )
Likelihood ratio test= 4.94  on 1 df,   p=0.02622
Wald test            = 2.67  on 1 df,   p=0.1023
Score (logrank) test = 3.32  on 1 df,   p=0.06842
Removed 1 rows containing missing values (geom_errorbar).



   
## Unadjusted Kaplan Meier Overall Survival Curve for:  AGE_40

Gender

uni_var(test_var = "SEX_F", data_imp = data)
_________________________________________________
   
## SEX_F
_________________________________________________
Call: survfit(formula = Surv(DX_LASTCONTACT_DEATH_MONTHS, PUF_VITAL_STATUS == 
    0) ~ SEX_F, data = data)

                n events median 0.95LCL 0.95UCL
SEX_F=Male    261     86   82.8    73.6      NA
SEX_F=Female 1099    243  155.4   130.5      NA

Call: survfit(formula = Surv(DX_LASTCONTACT_DEATH_MONTHS, PUF_VITAL_STATUS == 
    0) ~ SEX_F, data = data)

                SEX_F=Male 
 time n.risk n.event survival std.err lower 95% CI upper 95% CI
   12    223      11    0.956  0.0131        0.930        0.982
   24    185      16    0.883  0.0213        0.842        0.925
   36    144      13    0.816  0.0266        0.766        0.870
   48    115      12    0.744  0.0314        0.684        0.808
   60     89      12    0.662  0.0357        0.596        0.736
  120     17      20    0.435  0.0494        0.348        0.544

                SEX_F=Female 
 time n.risk n.event survival std.err lower 95% CI upper 95% CI
   12    961      37    0.964 0.00574        0.953        0.976
   24    847      36    0.927 0.00826        0.911        0.943
   36    720      27    0.895 0.01000        0.876        0.915
   48    609      22    0.866 0.01144        0.844        0.889
   60    503      32    0.817 0.01370        0.790        0.844
  120    108      75    0.619 0.02419        0.573        0.668




   
## Univariable Cox Proportional Hazard Model for:  SEX_F

Call:
coxph(formula = Surv(DX_LASTCONTACT_DEATH_MONTHS, PUF_VITAL_STATUS == 
    0) ~ SEX_F, data = data)

  n= 1360, number of events= 329 

               coef exp(coef) se(coef)      z Pr(>|z|)    
SEX_FFemale -0.6336    0.5307   0.1260 -5.029 4.93e-07 ***
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

            exp(coef) exp(-coef) lower .95 upper .95
SEX_FFemale    0.5307      1.884    0.4146    0.6793

Concordance= 0.555  (se = 0.012 )
Rsquare= 0.017   (max possible= 0.956 )
Likelihood ratio test= 22.71  on 1 df,   p=1.882e-06
Wald test            = 25.29  on 1 df,   p=4.927e-07
Score (logrank) test = 26.14  on 1 df,   p=3.175e-07
Removed 1 rows containing missing values (geom_errorbar).



   
## Unadjusted Kaplan Meier Overall Survival Curve for:  SEX_F

RACE_F

uni_var(test_var = "RACE_F", data_imp = data)
_________________________________________________
   
## RACE_F
_________________________________________________
Call: survfit(formula = Surv(DX_LASTCONTACT_DEATH_MONTHS, PUF_VITAL_STATUS == 
    0) ~ RACE_F, data = data)

                    n events median 0.95LCL 0.95UCL
RACE_F=White     1229    301    142   128.2      NA
RACE_F=Black       25      7     NA    71.5      NA
RACE_F=Other/Unk   35     11    111    97.2      NA
RACE_F=Asian       71     10     NA   108.1      NA

Call: survfit(formula = Surv(DX_LASTCONTACT_DEATH_MONTHS, PUF_VITAL_STATUS == 
    0) ~ RACE_F, data = data)

                RACE_F=White 
 time n.risk n.event survival std.err lower 95% CI upper 95% CI
   12   1075      41    0.965 0.00540        0.954        0.975
   24    938      50    0.918 0.00826        0.902        0.934
   36    787      36    0.880 0.01005        0.861        0.900
   48    653      32    0.842 0.01167        0.819        0.865
   60    533      41    0.785 0.01384        0.759        0.813
  120    115      85    0.591 0.02241        0.549        0.637

                RACE_F=Black 
 time n.risk n.event survival std.err lower 95% CI upper 95% CI
   12     21       2    0.918  0.0554        0.816        1.000
   24     17       1    0.870  0.0705        0.742        1.000
   36     15       0    0.870  0.0705        0.742        1.000
   48     13       2    0.754  0.0978        0.585        0.972
   60     11       1    0.691  0.1080        0.509        0.939

                RACE_F=Other/Unk 
 time n.risk n.event survival std.err lower 95% CI upper 95% CI
   12     30       2    0.938  0.0422        0.859        1.000
   24     26       1    0.905  0.0523        0.808        1.000
   36     20       2    0.824  0.0723        0.694        0.979
   48     18       0    0.824  0.0723        0.694        0.979
   60     16       1    0.779  0.0815        0.634        0.956
  120      3       5    0.363  0.1446        0.166        0.792

                RACE_F=Asian 
 time n.risk n.event survival std.err lower 95% CI upper 95% CI
   12     58       3    0.953  0.0265        0.903        1.000
   24     51       0    0.953  0.0265        0.903        1.000
   36     42       2    0.912  0.0380        0.841        0.990
   48     40       0    0.912  0.0380        0.841        0.990
   60     32       1    0.887  0.0446        0.804        0.979
  120      7       4    0.663  0.1136        0.474        0.927




   
## Univariable Cox Proportional Hazard Model for:  RACE_F

Call:
coxph(formula = Surv(DX_LASTCONTACT_DEATH_MONTHS, PUF_VITAL_STATUS == 
    0) ~ RACE_F, data = data)

  n= 1360, number of events= 329 

                   coef exp(coef) se(coef)      z Pr(>|z|)
RACE_FBlack      0.3512    1.4208   0.3828  0.917    0.359
RACE_FOther/Unk  0.1943    1.2145   0.3072  0.633    0.527
RACE_FAsian     -0.5282    0.5897   0.3215 -1.643    0.100

                exp(coef) exp(-coef) lower .95 upper .95
RACE_FBlack        1.4208     0.7038    0.6709     3.009
RACE_FOther/Unk    1.2145     0.8234    0.6652     2.218
RACE_FAsian        0.5897     1.6959    0.3140     1.107

Concordance= 0.516  (se = 0.009 )
Rsquare= 0.003   (max possible= 0.956 )
Likelihood ratio test= 4.48  on 3 df,   p=0.2138
Wald test            = 4.06  on 3 df,   p=0.2547
Score (logrank) test = 4.15  on 3 df,   p=0.2456
Removed 1 rows containing missing values (geom_errorbar).



   
## Unadjusted Kaplan Meier Overall Survival Curve for:  RACE_F

Hispanic

uni_var(test_var = "HISPANIC", data_imp = data)
_________________________________________________
   
## HISPANIC
_________________________________________________
Call: survfit(formula = Surv(DX_LASTCONTACT_DEATH_MONTHS, PUF_VITAL_STATUS == 
    0) ~ HISPANIC, data = data)

                    n events median 0.95LCL 0.95UCL
HISPANIC=No      1234    294    142   128.2      NA
HISPANIC=Yes       52      9     NA   125.6      NA
HISPANIC=Unknown   74     26     NA    97.2      NA

Call: survfit(formula = Surv(DX_LASTCONTACT_DEATH_MONTHS, PUF_VITAL_STATUS == 
    0) ~ HISPANIC, data = data)

                HISPANIC=No 
 time n.risk n.event survival std.err lower 95% CI upper 95% CI
   12   1069      44    0.962 0.00559        0.951        0.973
   24    929      44    0.921 0.00814        0.905        0.937
   36    773      36    0.882 0.01002        0.863        0.902
   48    645      29    0.847 0.01160        0.824        0.870
   60    525      40    0.790 0.01385        0.763        0.818
  120    106      88    0.575 0.02383        0.530        0.624

                HISPANIC=Yes 
 time n.risk n.event survival std.err lower 95% CI upper 95% CI
   12     47       0    1.000  0.0000        1.000        1.000
   24     41       4    0.913  0.0416        0.835        0.998
   36     36       1    0.890  0.0467        0.803        0.986
   48     28       1    0.864  0.0518        0.768        0.972
   60     23       1    0.831  0.0595        0.722        0.956
  120      9       0    0.831  0.0595        0.722        0.956

                HISPANIC=Unknown 
 time n.risk n.event survival std.err lower 95% CI upper 95% CI
   12     68       4    0.945  0.0268        0.894        0.999
   24     62       4    0.888  0.0372        0.818        0.964
   36     55       3    0.843  0.0435        0.762        0.933
   48     51       4    0.782  0.0500        0.690        0.886
   60     44       3    0.736  0.0537        0.637        0.849
  120     10       7    0.571  0.0706        0.448        0.728




   
## Univariable Cox Proportional Hazard Model for:  HISPANIC

Call:
coxph(formula = Surv(DX_LASTCONTACT_DEATH_MONTHS, PUF_VITAL_STATUS == 
    0) ~ HISPANIC, data = data)

  n= 1360, number of events= 329 

                   coef exp(coef) se(coef)      z Pr(>|z|)
HISPANICYes     -0.4176    0.6586   0.3385 -1.234    0.217
HISPANICUnknown  0.1136    1.1203   0.2050  0.554    0.580

                exp(coef) exp(-coef) lower .95 upper .95
HISPANICYes        0.6586     1.5184    0.3392     1.279
HISPANICUnknown    1.1203     0.8927    0.7496     1.674

Concordance= 0.514  (se = 0.009 )
Rsquare= 0.002   (max possible= 0.956 )
Likelihood ratio test= 2.12  on 2 df,   p=0.3459
Wald test            = 1.9  on 2 df,   p=0.3865
Score (logrank) test = 1.93  on 2 df,   p=0.3816
Removed 1 rows containing missing values (geom_errorbar).



   
## Unadjusted Kaplan Meier Overall Survival Curve for:  HISPANIC

Insurance Status

#uni_var(test_var = "HISTOLOGY_F_LIM", data_imp = data)

Overall Survival pre/post-ACA expansion

uni_var(test_var = "EXPN_GROUP", data_imp = no_Excludes)
_________________________________________________
   
## EXPN_GROUP
_________________________________________________
Call: survfit(formula = Surv(DX_LASTCONTACT_DEATH_MONTHS, PUF_VITAL_STATUS == 
    0) ~ EXPN_GROUP, data = no_Excludes)

                             n events median 0.95LCL 0.95UCL
EXPN_GROUP=Post-Expansion  248     36     NA    82.8      NA
EXPN_GROUP=Pre-Expansion  1137    303    142   128.2      NA

Call: survfit(formula = Surv(DX_LASTCONTACT_DEATH_MONTHS, PUF_VITAL_STATUS == 
    0) ~ EXPN_GROUP, data = no_Excludes)

                EXPN_GROUP=Post-Expansion 
 time n.risk n.event survival std.err lower 95% CI upper 95% CI
   12    206       8    0.965  0.0121        0.942        0.989
   24    144      16    0.882  0.0229        0.838        0.928
   36     79       6    0.838  0.0280        0.785        0.894
   48     52       3    0.797  0.0351        0.731        0.869
   60     33       1    0.780  0.0384        0.708        0.859

                EXPN_GROUP=Pre-Expansion 
 time n.risk n.event survival std.err lower 95% CI upper 95% CI
   12   1000      41    0.962 0.00580        0.951        0.974
   24    900      41    0.921 0.00835        0.905        0.938
   36    799      34    0.885 0.01009        0.865        0.905
   48    683      33    0.846 0.01167        0.824        0.870
   60    568      44    0.789 0.01376        0.762        0.816
  120    125      94    0.586 0.02213        0.545        0.631




   
## Univariable Cox Proportional Hazard Model for:  EXPN_GROUP

Call:
coxph(formula = Surv(DX_LASTCONTACT_DEATH_MONTHS, PUF_VITAL_STATUS == 
    0) ~ EXPN_GROUP, data = no_Excludes)

  n= 1385, number of events= 339 

                           coef exp(coef) se(coef)      z Pr(>|z|)
EXPN_GROUPPre-Expansion -0.2137    0.8076   0.1815 -1.178    0.239

                        exp(coef) exp(-coef) lower .95 upper .95
EXPN_GROUPPre-Expansion    0.8076      1.238    0.5659     1.153

Concordance= 0.514  (se = 0.01 )
Rsquare= 0.001   (max possible= 0.958 )
Likelihood ratio test= 1.32  on 1 df,   p=0.2507
Wald test            = 1.39  on 1 df,   p=0.239
Score (logrank) test = 1.39  on 1 df,   p=0.2381





   
## Unadjusted Kaplan Meier Overall Survival Curve for:  EXPN_GROUP

Education

uni_var(test_var = "EDUCATION_F", data_imp = data)
_________________________________________________
   
## EDUCATION_F
_________________________________________________
Call: survfit(formula = Surv(DX_LASTCONTACT_DEATH_MONTHS, PUF_VITAL_STATUS == 
    0) ~ EDUCATION_F, data = data)

   5 observations deleted due to missingness 
                           n events median 0.95LCL 0.95UCL
EDUCATION_F=21% or more  167     43     NA     114      NA
EDUCATION_F=13 - 20.9%   320     72     NA     116      NA
EDUCATION_F=7 - 12.9%    465    132    132     108      NA
EDUCATION_F=Less than 7% 403     81    155     133      NA

Call: survfit(formula = Surv(DX_LASTCONTACT_DEATH_MONTHS, PUF_VITAL_STATUS == 
    0) ~ EDUCATION_F, data = data)

5 observations deleted due to missingness 
                EDUCATION_F=21% or more 
 time n.risk n.event survival std.err lower 95% CI upper 95% CI
   12    145       8    0.949  0.0175        0.916        0.984
   24    121      10    0.879  0.0268        0.828        0.933
   36    105       4    0.848  0.0301        0.791        0.909
   48     90       3    0.823  0.0325        0.762        0.889
   60     75       4    0.784  0.0362        0.716        0.859
  120     18      12    0.597  0.0576        0.494        0.721

                EDUCATION_F=13 - 20.9% 
 time n.risk n.event survival std.err lower 95% CI upper 95% CI
   12    275      13    0.957  0.0117        0.934        0.980
   24    240      13    0.910  0.0168        0.878        0.944
   36    198       7    0.881  0.0196        0.844        0.920
   48    171       6    0.853  0.0220        0.811        0.898
   60    138      10    0.800  0.0264        0.750        0.853
  120     25      20    0.583  0.0491        0.494        0.687

                EDUCATION_F=7 - 12.9% 
 time n.risk n.event survival std.err lower 95% CI upper 95% CI
   12    401      20    0.955 0.00986        0.936        0.974
   24    352      14    0.920 0.01316        0.895        0.946
   36    291      20    0.864 0.01735        0.831        0.899
   48    242      16    0.813 0.02045        0.774        0.855
   60    192      21    0.738 0.02431        0.692        0.787
  120     44      35    0.545 0.03483        0.481        0.618

                EDUCATION_F=Less than 7% 
 time n.risk n.event survival std.err lower 95% CI upper 95% CI
   12    361       7    0.982 0.00689        0.968        0.995
   24    317      15    0.939 0.01268        0.914        0.964
   36    268       9    0.911 0.01540        0.881        0.941
   48    221       8    0.881 0.01810        0.846        0.917
   60    187       9    0.843 0.02138        0.802        0.886
  120     38      28    0.634 0.04128        0.558        0.721




   
## Univariable Cox Proportional Hazard Model for:  EDUCATION_F

Call:
coxph(formula = Surv(DX_LASTCONTACT_DEATH_MONTHS, PUF_VITAL_STATUS == 
    0) ~ EDUCATION_F, data = data)

  n= 1355, number of events= 328 
   (5 observations deleted due to missingness)

                           coef exp(coef) se(coef)      z Pr(>|z|)
EDUCATION_F13 - 20.9%   -0.0936    0.9106   0.1928 -0.485    0.627
EDUCATION_F7 - 12.9%     0.1585    1.1718   0.1757  0.902    0.367
EDUCATION_FLess than 7% -0.2447    0.7829   0.1888 -1.296    0.195

                        exp(coef) exp(-coef) lower .95 upper .95
EDUCATION_F13 - 20.9%      0.9106     1.0981    0.6241     1.329
EDUCATION_F7 - 12.9%       1.1718     0.8534    0.8305     1.653
EDUCATION_FLess than 7%    0.7829     1.2773    0.5408     1.133

Concordance= 0.552  (se = 0.017 )
Rsquare= 0.006   (max possible= 0.956 )
Likelihood ratio test= 8.76  on 3 df,   p=0.03268
Wald test            = 8.7  on 3 df,   p=0.03359
Score (logrank) test = 8.78  on 3 df,   p=0.03229
Removed 1 rows containing missing values (geom_errorbar).



   
## Unadjusted Kaplan Meier Overall Survival Curve for:  EDUCATION_F

Urban/Rural

uni_var(test_var = "U_R_F", data_imp = data)
_________________________________________________
   
## U_R_F
_________________________________________________
Call: survfit(formula = Surv(DX_LASTCONTACT_DEATH_MONTHS, PUF_VITAL_STATUS == 
    0) ~ U_R_F, data = data)

   40 observations deleted due to missingness 
               n events median 0.95LCL 0.95UCL
U_R_F=Metro 1106    264    150   128.2      NA
U_R_F=Urban  187     50    133   108.0      NA
U_R_F=Rural   27      6     NA    97.2      NA

Call: survfit(formula = Surv(DX_LASTCONTACT_DEATH_MONTHS, PUF_VITAL_STATUS == 
    0) ~ U_R_F, data = data)

40 observations deleted due to missingness 
                U_R_F=Metro 
 time n.risk n.event survival std.err lower 95% CI upper 95% CI
   12    965      39    0.963 0.00586        0.951        0.974
   24    843      39    0.922 0.00850        0.906        0.939
   36    709      32    0.884 0.01043        0.864        0.905
   48    593      25    0.851 0.01201        0.828        0.875
   60    482      40    0.790 0.01456        0.762        0.819
  120    105      75    0.593 0.02410        0.547        0.642

                U_R_F=Urban 
 time n.risk n.event survival std.err lower 95% CI upper 95% CI
   12    164       6    0.966  0.0135        0.940        0.993
   24    139      11    0.899  0.0233        0.854        0.946
   36    116       7    0.850  0.0284        0.796        0.908
   48     99       6    0.805  0.0324        0.744        0.871
   60     82       3    0.780  0.0345        0.715        0.850
  120     14      15    0.561  0.0582        0.458        0.688

                U_R_F=Rural 
 time n.risk n.event survival std.err lower 95% CI upper 95% CI
   12     25       0    1.000  0.0000        1.000         1.00
   24     24       1    0.960  0.0392        0.886         1.00
   36     19       1    0.912  0.0598        0.802         1.00
   48     15       1    0.855  0.0787        0.714         1.00
   60     13       0    0.855  0.0787        0.714         1.00
  120      2       3    0.528  0.1612        0.290         0.96




   
## Univariable Cox Proportional Hazard Model for:  U_R_F

Call:
coxph(formula = Surv(DX_LASTCONTACT_DEATH_MONTHS, PUF_VITAL_STATUS == 
    0) ~ U_R_F, data = data)

  n= 1320, number of events= 320 
   (40 observations deleted due to missingness)

              coef exp(coef) se(coef)      z Pr(>|z|)
U_R_FUrban  0.1540    1.1665   0.1544  0.997    0.319
U_R_FRural -0.1929    0.8246   0.4129 -0.467    0.640

           exp(coef) exp(-coef) lower .95 upper .95
U_R_FUrban    1.1665     0.8573    0.8618     1.579
U_R_FRural    0.8246     1.2128    0.3671     1.852

Concordance= 0.516  (se = 0.012 )
Rsquare= 0.001   (max possible= 0.955 )
Likelihood ratio test= 1.25  on 2 df,   p=0.5341
Wald test            = 1.27  on 2 df,   p=0.5292
Score (logrank) test = 1.28  on 2 df,   p=0.5283
Removed 1 rows containing missing values (geom_errorbar).



   
## Unadjusted Kaplan Meier Overall Survival Curve for:  U_R_F

Class (treatment at performing facility)

uni_var(test_var = "CLASS_OF_CASE_F", data_imp = data)
_________________________________________________
   
## CLASS_OF_CASE_F
_________________________________________________
Call: survfit(formula = Surv(DX_LASTCONTACT_DEATH_MONTHS, PUF_VITAL_STATUS == 
    0) ~ CLASS_OF_CASE_F, data = data)

                                  n events median 0.95LCL 0.95UCL
CLASS_OF_CASE_F=Other_Facility   27      6   84.9    53.5      NA
CLASS_OF_CASE_F=All_Part_Prim  1333    323  149.6   128.5      NA

Call: survfit(formula = Surv(DX_LASTCONTACT_DEATH_MONTHS, PUF_VITAL_STATUS == 
    0) ~ CLASS_OF_CASE_F, data = data)

                CLASS_OF_CASE_F=Other_Facility 
 time n.risk n.event survival std.err lower 95% CI upper 95% CI
   12     18       0    1.000   0.000        1.000            1
   24     13       1    0.944   0.054        0.844            1
   36     12       0    0.944   0.054        0.844            1
   48      7       1    0.850   0.102        0.672            1
   60      6       1    0.729   0.142        0.497            1
  120      1       2    0.389   0.196        0.144            1

                CLASS_OF_CASE_F=All_Part_Prim 
 time n.risk n.event survival std.err lower 95% CI upper 95% CI
   12   1166      48    0.962 0.00536        0.952        0.973
   24   1019      51    0.918 0.00790        0.903        0.934
   36    852      40    0.879 0.00967        0.861        0.898
   48    717      33    0.843 0.01114        0.822        0.865
   60    586      43    0.789 0.01314        0.764        0.815
  120    124      93    0.588 0.02198        0.547        0.633




   
## Univariable Cox Proportional Hazard Model for:  CLASS_OF_CASE_F

Call:
coxph(formula = Surv(DX_LASTCONTACT_DEATH_MONTHS, PUF_VITAL_STATUS == 
    0) ~ CLASS_OF_CASE_F, data = data)

  n= 1360, number of events= 329 

                                coef exp(coef) se(coef)      z Pr(>|z|)
CLASS_OF_CASE_FAll_Part_Prim -0.5015    0.6056   0.4126 -1.216    0.224

                             exp(coef) exp(-coef) lower .95 upper .95
CLASS_OF_CASE_FAll_Part_Prim    0.6056      1.651    0.2698      1.36

Concordance= 0.501  (se = 0.004 )
Rsquare= 0.001   (max possible= 0.956 )
Likelihood ratio test= 1.27  on 1 df,   p=0.2606
Wald test            = 1.48  on 1 df,   p=0.2242
Score (logrank) test = 1.51  on 1 df,   p=0.2193
Removed 1 rows containing missing values (geom_errorbar).



   
## Unadjusted Kaplan Meier Overall Survival Curve for:  CLASS_OF_CASE_F

Year

uni_var(test_var = "YEAR_OF_DIAGNOSIS", data_imp = data)
_________________________________________________
   
## YEAR_OF_DIAGNOSIS
_________________________________________________
Call: survfit(formula = Surv(DX_LASTCONTACT_DEATH_MONTHS, PUF_VITAL_STATUS == 
    0) ~ YEAR_OF_DIAGNOSIS, data = data)

                         n events median 0.95LCL 0.95UCL
YEAR_OF_DIAGNOSIS=2004  84     29     NA   141.8      NA
YEAR_OF_DIAGNOSIS=2005  89     37     NA    97.4      NA
YEAR_OF_DIAGNOSIS=2006 102     34     NA   124.8      NA
YEAR_OF_DIAGNOSIS=2007 114     43  125.1   112.8      NA
YEAR_OF_DIAGNOSIS=2008 123     39     NA   106.7      NA
YEAR_OF_DIAGNOSIS=2009 110     28     NA   100.4      NA
YEAR_OF_DIAGNOSIS=2010 117     26     NA      NA      NA
YEAR_OF_DIAGNOSIS=2011 125     29   78.3    78.3      NA
YEAR_OF_DIAGNOSIS=2012 103     16   71.5    68.4      NA
YEAR_OF_DIAGNOSIS=2013 118     24     NA      NA      NA
YEAR_OF_DIAGNOSIS=2014 121     12     NA      NA      NA
YEAR_OF_DIAGNOSIS=2015 154     12     NA      NA      NA

Call: survfit(formula = Surv(DX_LASTCONTACT_DEATH_MONTHS, PUF_VITAL_STATUS == 
    0) ~ YEAR_OF_DIAGNOSIS, data = data)

                YEAR_OF_DIAGNOSIS=2004 
 time n.risk n.event survival std.err lower 95% CI upper 95% CI
   12     77       2    0.975  0.0173        0.942        1.000
   24     75       1    0.963  0.0212        0.922        1.000
   36     71       4    0.911  0.0320        0.851        0.976
   48     67       3    0.872  0.0378        0.801        0.949
   60     63       4    0.820  0.0436        0.739        0.910
  120     38       7    0.716  0.0531        0.619        0.828

                YEAR_OF_DIAGNOSIS=2005 
 time n.risk n.event survival std.err lower 95% CI upper 95% CI
   12     76       6    0.931  0.0271        0.879        0.986
   24     72       4    0.882  0.0351        0.816        0.954
   36     70       1    0.870  0.0367        0.801        0.945
   48     64       4    0.819  0.0424        0.740        0.907
   60     58       6    0.742  0.0487        0.653        0.844
  120     37      13    0.563  0.0570        0.462        0.687

                YEAR_OF_DIAGNOSIS=2006 
 time n.risk n.event survival std.err lower 95% CI upper 95% CI
   12     86       4    0.959  0.0201        0.921        0.999
   24     80       3    0.925  0.0273        0.873        0.980
   36     74       3    0.889  0.0331        0.827        0.957
   48     67       5    0.829  0.0405        0.753        0.912
   60     60       4    0.779  0.0450        0.696        0.873
  120     36      12    0.607  0.0563        0.506        0.728

                YEAR_OF_DIAGNOSIS=2007 
 time n.risk n.event survival std.err lower 95% CI upper 95% CI
   12    106       4    0.964  0.0175        0.931        0.999
   24     98       6    0.909  0.0275        0.857        0.964
   36     94       2    0.890  0.0299        0.833        0.951
   48     91       3    0.862  0.0332        0.799        0.929
   60     84       6    0.805  0.0383        0.733        0.883
  120     14      20    0.553  0.0569        0.452        0.677

                YEAR_OF_DIAGNOSIS=2008 
 time n.risk n.event survival std.err lower 95% CI upper 95% CI
   12    112       6    0.950  0.0200        0.911        0.990
   24    103       3    0.923  0.0247        0.876        0.973
   36     94       7    0.859  0.0327        0.797        0.926
   48     84       5    0.813  0.0370        0.743        0.888
   60     83       0    0.813  0.0370        0.743        0.888

                YEAR_OF_DIAGNOSIS=2009 
 time n.risk n.event survival std.err lower 95% CI upper 95% CI
   12     99       3    0.971  0.0163        0.940        1.000
   24     94       3    0.942  0.0232        0.897        0.988
   36     91       1    0.931  0.0251        0.883        0.982
   48     83       5    0.879  0.0328        0.817        0.946
   60     71       7    0.801  0.0411        0.724        0.885

                YEAR_OF_DIAGNOSIS=2010 
 time n.risk n.event survival std.err lower 95% CI upper 95% CI
   12    105       4    0.965  0.0173        0.931        0.999
   24     95       6    0.909  0.0275        0.857        0.964
   36     88       4    0.870  0.0325        0.808        0.936
   48     84       1    0.860  0.0337        0.796        0.928
   60     78       4    0.818  0.0379        0.748        0.896

                YEAR_OF_DIAGNOSIS=2011 
 time n.risk n.event survival std.err lower 95% CI upper 95% CI
   12    110       3    0.975  0.0142        0.948        1.000
   24    104       4    0.939  0.0223        0.896        0.984
   36    101       2    0.921  0.0253        0.873        0.972
   48     90       2    0.902  0.0283        0.848        0.959
   60     66      12    0.774  0.0420        0.695        0.861

                YEAR_OF_DIAGNOSIS=2012 
 time n.risk n.event survival std.err lower 95% CI upper 95% CI
   12     91       2    0.979  0.0148        0.950        1.000
   24     84       3    0.946  0.0235        0.901        0.993
   36     74       5    0.889  0.0333        0.826        0.956
   48     63       2    0.864  0.0366        0.795        0.939
   60     29       1    0.848  0.0391        0.775        0.929

                YEAR_OF_DIAGNOSIS=2013 
 time n.risk n.event survival std.err lower 95% CI upper 95% CI
   12    104       7    0.938  0.0226        0.895        0.984
   24     92       6    0.882  0.0308        0.824        0.944
   36     77       8    0.802  0.0390        0.729        0.882
   48     31       3    0.750  0.0467        0.664        0.848

                YEAR_OF_DIAGNOSIS=2014 
 time n.risk n.event survival std.err lower 95% CI upper 95% CI
   12     96       4    0.963  0.0183        0.928        0.999
   24     81       5    0.911  0.0285        0.857        0.968
   36     30       2    0.888  0.0321        0.827        0.953

                YEAR_OF_DIAGNOSIS=2015 
 time n.risk n.event survival std.err lower 95% CI upper 95% CI
   12    122       3    0.978  0.0128        0.953        1.000
   24     54       8    0.888  0.0329        0.826        0.955




   
## Univariable Cox Proportional Hazard Model for:  YEAR_OF_DIAGNOSIS
X matrix deemed to be singular; variable 12
Call:
coxph(formula = Surv(DX_LASTCONTACT_DEATH_MONTHS, PUF_VITAL_STATUS == 
    0) ~ YEAR_OF_DIAGNOSIS, data = data)

  n= 1360, number of events= 329 

                        coef exp(coef) se(coef)     z Pr(>|z|)   
YEAR_OF_DIAGNOSIS2005 0.3673    1.4439   0.2519 1.458  0.14482   
YEAR_OF_DIAGNOSIS2006 0.2749    1.3164   0.2582 1.065  0.28707   
YEAR_OF_DIAGNOSIS2007 0.4297    1.5369   0.2491 1.725  0.08450 . 
YEAR_OF_DIAGNOSIS2008 0.3854    1.4702   0.2554 1.509  0.13125   
YEAR_OF_DIAGNOSIS2009 0.2419    1.2737   0.2760 0.877  0.38075   
YEAR_OF_DIAGNOSIS2010 0.2608    1.2979   0.2823 0.924  0.35568   
YEAR_OF_DIAGNOSIS2011 0.4533    1.5736   0.2769 1.637  0.10157   
YEAR_OF_DIAGNOSIS2012 0.2886    1.3346   0.3253 0.887  0.37490   
YEAR_OF_DIAGNOSIS2013 0.8195    2.2693   0.2945 2.783  0.00539 **
YEAR_OF_DIAGNOSIS2014 0.4811    1.6178   0.3615 1.331  0.18331   
YEAR_OF_DIAGNOSIS2015 0.5897    1.8035   0.3659 1.612  0.10701   
YEAR_OF_DIAGNOSIS2016     NA        NA   0.0000    NA       NA   
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

                      exp(coef) exp(-coef) lower .95 upper .95
YEAR_OF_DIAGNOSIS2005     1.444     0.6926    0.8812     2.366
YEAR_OF_DIAGNOSIS2006     1.316     0.7597    0.7936     2.184
YEAR_OF_DIAGNOSIS2007     1.537     0.6507    0.9432     2.504
YEAR_OF_DIAGNOSIS2008     1.470     0.6802    0.8913     2.425
YEAR_OF_DIAGNOSIS2009     1.274     0.7851    0.7415     2.188
YEAR_OF_DIAGNOSIS2010     1.298     0.7705    0.7463     2.257
YEAR_OF_DIAGNOSIS2011     1.574     0.6355    0.9145     2.708
YEAR_OF_DIAGNOSIS2012     1.335     0.7493    0.7055     2.525
YEAR_OF_DIAGNOSIS2013     2.269     0.4407    1.2742     4.042
YEAR_OF_DIAGNOSIS2014     1.618     0.6181    0.7965     3.286
YEAR_OF_DIAGNOSIS2015     1.803     0.5545    0.8804     3.694
YEAR_OF_DIAGNOSIS2016        NA         NA        NA        NA

Concordance= 0.542  (se = 0.018 )
Rsquare= 0.007   (max possible= 0.956 )
Likelihood ratio test= 9.57  on 11 df,   p=0.5696
Wald test            = 9.7  on 11 df,   p=0.558
Score (logrank) test = 9.85  on 11 df,   p=0.5435
Removed 2 rows containing missing values (geom_errorbar).



   
## Unadjusted Kaplan Meier Overall Survival Curve for:  YEAR_OF_DIAGNOSIS
This manual palette can handle a maximum of 10 values. You have supplied 12.

Primary Site

uni_var(test_var = "SITE_TEXT", data_imp = data)
_________________________________________________
   
## SITE_TEXT
_________________________________________________
Call: survfit(formula = Surv(DX_LASTCONTACT_DEATH_MONTHS, PUF_VITAL_STATUS == 
    0) ~ SITE_TEXT, data = data)

                                                            n events median 0.95LCL 0.95UCL
SITE_TEXT=C44.2 External ear                                2      0     NA      NA      NA
SITE_TEXT=C44.3 Skin of ear and unspecified parts of face   2      1   7.95      NA      NA
SITE_TEXT=C44.4 Skin of scalp and neck                      3      0     NA      NA      NA
SITE_TEXT=C44.5 Skin of trunk                             287     86 131.91    81.2      NA
SITE_TEXT=C44.6 Skin of upper limb and shoulder             7      2     NA    50.2      NA
SITE_TEXT=C44.7 Skin of lower limb and hip                 12      5  81.81    70.2      NA
SITE_TEXT=C44.8 Overlapping lesion of skin                 11      7  32.79    20.3      NA
SITE_TEXT=C44.9 Skin, NOS                                  19     10  55.92    30.2      NA
SITE_TEXT=C51.0 Labium majus                               92     16     NA      NA      NA
SITE_TEXT=C51.1 Labium minus                               15      3     NA    85.4      NA
SITE_TEXT=C51.2 Clitoris                                    2      0     NA      NA      NA
SITE_TEXT=C51.8 Overlapping lesion of vulva                60     16 149.62   128.5      NA
SITE_TEXT=C51.9 Vulva, NOS                                822    176 155.43   128.2      NA
SITE_TEXT=C52.9 Vagina, NOS                                 1      0     NA      NA      NA
SITE_TEXT=C60.2 Body of penis                               1      0     NA      NA      NA
SITE_TEXT=C60.8 Overlapping lesion of penis                 2      0     NA      NA      NA
SITE_TEXT=C60.9 Penis                                      22      7 108.06    73.2      NA

Call: survfit(formula = Surv(DX_LASTCONTACT_DEATH_MONTHS, PUF_VITAL_STATUS == 
    0) ~ SITE_TEXT, data = data)

                SITE_TEXT=C44.2 External ear 
 time n.risk n.event survival std.err lower 95% CI upper 95% CI
   12      2       0        1       0            1            1
   24      2       0        1       0            1            1
   36      1       0        1       0            1            1
   48      1       0        1       0            1            1
   60      1       0        1       0            1            1
  120      1       0        1       0            1            1

                SITE_TEXT=C44.3 Skin of ear and unspecified parts of face 
     time n.risk n.event survival std.err lower 95% CI upper 95% CI

                SITE_TEXT=C44.4 Skin of scalp and neck 
 time n.risk n.event survival std.err lower 95% CI upper 95% CI
   12      3       0        1       0            1            1
   24      3       0        1       0            1            1
   36      2       0        1       0            1            1
   48      2       0        1       0            1            1
   60      2       0        1       0            1            1

                SITE_TEXT=C44.5 Skin of trunk 
 time n.risk n.event survival std.err lower 95% CI upper 95% CI
   12    245      11    0.960  0.0119        0.937        0.983
   24    207      15    0.898  0.0191        0.861        0.936
   36    165      14    0.833  0.0243        0.787        0.882
   48    129      13    0.763  0.0291        0.708        0.822
   60    104      11    0.694  0.0330        0.633        0.762
  120     17      20    0.503  0.0452        0.422        0.600

                SITE_TEXT=C44.6 Skin of upper limb and shoulder 
 time n.risk n.event survival std.err lower 95% CI upper 95% CI
   12      6       1    0.857   0.132        0.633            1
   24      3       0    0.857   0.132        0.633            1
   36      3       0    0.857   0.132        0.633            1
   48      3       0    0.857   0.132        0.633            1
   60      2       1    0.571   0.249        0.243            1

                SITE_TEXT=C44.7 Skin of lower limb and hip 
 time n.risk n.event survival std.err lower 95% CI upper 95% CI
   12     10       1    0.909  0.0867       0.7541            1
   24      9       1    0.818  0.1163       0.6192            1
   36      4       0    0.818  0.1163       0.6192            1
   48      4       0    0.818  0.1163       0.6192            1
   60      4       0    0.818  0.1163       0.6192            1
  120      1       2    0.273  0.2260       0.0537            1

                SITE_TEXT=C44.8 Overlapping lesion of skin 
 time n.risk n.event survival std.err lower 95% CI upper 95% CI
   12      8       2      0.8   0.126        0.587        1.000
   24      5       3      0.5   0.158        0.269        0.929
   36      5       0      0.5   0.158        0.269        0.929
   48      4       1      0.4   0.155        0.187        0.855
   60      4       0      0.4   0.155        0.187        0.855

                SITE_TEXT=C44.9 Skin, NOS 
 time n.risk n.event survival std.err lower 95% CI upper 95% CI
   12     16       1    0.947  0.0512        0.852        1.000
   24     12       2    0.820  0.0953        0.653        1.000
   36      8       3    0.615  0.1249        0.413        0.916
   48      7       0    0.615  0.1249        0.413        0.916
   60      4       3    0.351  0.1354        0.165        0.748
  120      2       1    0.264  0.1269        0.103        0.677

                SITE_TEXT=C51.0 Labium majus 
 time n.risk n.event survival std.err lower 95% CI upper 95% CI
   12     79       2    0.977  0.0159        0.947        1.000
   24     68       3    0.938  0.0268        0.887        0.992
   36     60       0    0.938  0.0268        0.887        0.992
   48     49       2    0.904  0.0350        0.838        0.976
   60     47       0    0.904  0.0350        0.838        0.976
  120     12       9    0.661  0.0785        0.523        0.834

                SITE_TEXT=C51.1 Labium minus 
 time n.risk n.event survival std.err lower 95% CI upper 95% CI
   12     13       1    0.929  0.0688        0.803            1
   24     11       0    0.929  0.0688        0.803            1
   36     10       1    0.844  0.1019        0.666            1
   48      8       0    0.844  0.1019        0.666            1
   60      7       0    0.844  0.1019        0.666            1
  120      1       1    0.703  0.1540        0.458            1

                SITE_TEXT=C51.2 Clitoris 
 time n.risk n.event survival std.err lower 95% CI upper 95% CI
   12      2       0        1       0            1            1
   24      1       0        1       0            1            1
   36      1       0        1       0            1            1
   48      1       0        1       0            1            1
   60      1       0        1       0            1            1

                SITE_TEXT=C51.8 Overlapping lesion of vulva 
 time n.risk n.event survival std.err lower 95% CI upper 95% CI
   12     49       3    0.946  0.0303        0.888        1.000
   24     49       0    0.946  0.0303        0.888        1.000
   36     42       3    0.884  0.0447        0.801        0.976
   48     36       2    0.840  0.0524        0.743        0.949
   60     32       1    0.814  0.0566        0.711        0.933
  120      9       5    0.645  0.0833        0.501        0.831

                SITE_TEXT=C51.9 Vulva, NOS 
 time n.risk n.event survival std.err lower 95% CI upper 95% CI
   12    726      24    0.969 0.00618        0.957        0.981
   24    643      27    0.932 0.00922        0.914        0.950
   36    545      19    0.902 0.01119        0.880        0.924
   48    464      15    0.876 0.01278        0.851        0.901
   60    373      27    0.820 0.01580        0.790        0.852
  120     80      53    0.620 0.02887        0.566        0.679

                SITE_TEXT=C52.9 Vagina, NOS 
 time n.risk n.event survival std.err lower 95% CI upper 95% CI
   12      1       0        1       0            1            1
   24      1       0        1       0            1            1
   36      1       0        1       0            1            1
   48      1       0        1       0            1            1

                SITE_TEXT=C60.2 Body of penis 
 time n.risk n.event survival std.err lower 95% CI upper 95% CI
   12      1       0        1       0            1            1
   24      1       0        1       0            1            1
   36      1       0        1       0            1            1
   48      1       0        1       0            1            1
   60      1       0        1       0            1            1

                SITE_TEXT=C60.8 Overlapping lesion of penis 
 time n.risk n.event survival std.err lower 95% CI upper 95% CI
   12      2       0        1       0            1            1
   24      1       0        1       0            1            1
   36      1       0        1       0            1            1
   48      1       0        1       0            1            1
   60      1       0        1       0            1            1
  120      1       0        1       0            1            1

                SITE_TEXT=C60.9 Penis 
 time n.risk n.event survival std.err lower 95% CI upper 95% CI
   12     21       1    0.955  0.0444       0.8714            1
   24     16       1    0.898  0.0687       0.7734            1
   36     15       0    0.898  0.0687       0.7734            1
   48     13       1    0.834  0.0888       0.6771            1
   60      9       1    0.770  0.1026       0.5931            1
  120      1       3    0.257  0.1990       0.0562            1




   
## Univariable Cox Proportional Hazard Model for:  SITE_TEXT
Loglik converged before variable  45,47,56,59,62,63 ; beta may be infinite. X matrix deemed to be singular; variable 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 53 60 61 64 65 66 67 68 69 70 71 72 73
Call:
coxph(formula = Surv(DX_LASTCONTACT_DEATH_MONTHS, PUF_VITAL_STATUS == 
    0) ~ SITE_TEXT, data = data)

  n= 1360, number of events= 329 

                                                               coef  exp(coef)   se(coef)      z Pr(>|z|)   
SITE_TEXTC00.1 External Lip: Lower NOS                           NA         NA  0.000e+00     NA       NA   
SITE_TEXTC00.2 External Lip: NOS                                 NA         NA  0.000e+00     NA       NA   
SITE_TEXTC00.3 Lip: Upper Mucosa                                 NA         NA  0.000e+00     NA       NA   
SITE_TEXTC00.4 Lip: Lower Mucosa                                 NA         NA  0.000e+00     NA       NA   
SITE_TEXTC00.5 Lip: Mucosa NOS                                   NA         NA  0.000e+00     NA       NA   
SITE_TEXTC00.6 Lip: Commissure                                   NA         NA  0.000e+00     NA       NA   
SITE_TEXTC00.8 Lip: Overlapping                                  NA         NA  0.000e+00     NA       NA   
SITE_TEXTC00.9 Lip NOS                                           NA         NA  0.000e+00     NA       NA   
SITE_TEXTC01.9 Tongue: Base NOS                                  NA         NA  0.000e+00     NA       NA   
SITE_TEXTC02.0 Tongue: Dorsal NOS                                NA         NA  0.000e+00     NA       NA   
SITE_TEXTC02.1 Tongue: Border, Tip                               NA         NA  0.000e+00     NA       NA   
SITE_TEXTC02.2 Tongue: Ventral NOS                               NA         NA  0.000e+00     NA       NA   
SITE_TEXTC02.3 Tongue: Anterior NOS                              NA         NA  0.000e+00     NA       NA   
SITE_TEXTC02.4 Lingual Tonsil                                    NA         NA  0.000e+00     NA       NA   
SITE_TEXTC02.8 Tongue: Overlapping                               NA         NA  0.000e+00     NA       NA   
SITE_TEXTC02.9 Tongue: NOS                                       NA         NA  0.000e+00     NA       NA   
SITE_TEXTC03.0 Gum: Upper                                        NA         NA  0.000e+00     NA       NA   
SITE_TEXTC03.1 Gum: Lower                                        NA         NA  0.000e+00     NA       NA   
SITE_TEXTC03.9 Gum NOS                                           NA         NA  0.000e+00     NA       NA   
SITE_TEXTC04.0 Mouth: Anterior Floor                             NA         NA  0.000e+00     NA       NA   
SITE_TEXTC04.1 Mouth: Lateral Floor                              NA         NA  0.000e+00     NA       NA   
SITE_TEXTC04.9 Floor of Mouth NOS                                NA         NA  0.000e+00     NA       NA   
SITE_TEXTC05.0 Hard Palate                                       NA         NA  0.000e+00     NA       NA   
SITE_TEXTC05.1 Soft Palate NOS                                   NA         NA  0.000e+00     NA       NA   
SITE_TEXTC05.2 Uvula                                             NA         NA  0.000e+00     NA       NA   
SITE_TEXTC05.8 Palate: Overlapping                               NA         NA  0.000e+00     NA       NA   
SITE_TEXTC05.9 Palate NOS                                        NA         NA  0.000e+00     NA       NA   
SITE_TEXTC06.0 Cheek Mucosa                                      NA         NA  0.000e+00     NA       NA   
SITE_TEXTC06.1 Mouth: Vestibule                                  NA         NA  0.000e+00     NA       NA   
SITE_TEXTC06.2 Retromolar Area                                   NA         NA  0.000e+00     NA       NA   
SITE_TEXTC06.8 Mouth: Other Overlapping                          NA         NA  0.000e+00     NA       NA   
SITE_TEXTC06.9 Mouth NOS                                         NA         NA  0.000e+00     NA       NA   
SITE_TEXTC07.9 Parotid Gland                                     NA         NA  0.000e+00     NA       NA   
SITE_TEXTC09.8 Tonsil: Overlapping                               NA         NA  0.000e+00     NA       NA   
SITE_TEXTC09.9 Tonsil NOS                                        NA         NA  0.000e+00     NA       NA   
SITE_TEXTC11.1 Nasopharynx: Poster Wall                          NA         NA  0.000e+00     NA       NA   
SITE_TEXTC14.2 Waldeyer Ring                                     NA         NA  0.000e+00     NA       NA   
SITE_TEXTC30.0 Nasal Cavity                                      NA         NA  0.000e+00     NA       NA   
SITE_TEXTC37.9 Thymus                                            NA         NA  0.000e+00     NA       NA   
SITE_TEXTC42.0 Blood                                             NA         NA  0.000e+00     NA       NA   
SITE_TEXTC42.2 Spleen                                            NA         NA  0.000e+00     NA       NA   
SITE_TEXTC42.4 Hematopoietic NOS                                 NA         NA  0.000e+00     NA       NA   
SITE_TEXTC44.0 Skin of lip, NOS                                  NA         NA  0.000e+00     NA       NA   
SITE_TEXTC44.1 Eyelid                                            NA         NA  0.000e+00     NA       NA   
SITE_TEXTC44.2 External ear                              -1.556e+01  1.740e-07  2.273e+03 -0.007   0.9945   
SITE_TEXTC44.3 Skin of ear and unspecified parts of face  3.212e+00  2.482e+01  1.086e+00  2.958   0.0031 **
SITE_TEXTC44.4 Skin of scalp and neck                    -1.555e+01  1.769e-07  2.329e+03 -0.007   0.9947   
SITE_TEXTC44.5 Skin of trunk                              5.459e-02  1.056e+00  3.932e-01  0.139   0.8896   
SITE_TEXTC44.6 Skin of upper limb and shoulder            2.825e-01  1.326e+00  8.020e-01  0.352   0.7247   
SITE_TEXTC44.7 Skin of lower limb and hip                 5.495e-01  1.732e+00  5.859e-01  0.938   0.3483   
SITE_TEXTC44.8 Overlapping lesion of skin                 1.017e+00  2.766e+00  5.348e-01  1.902   0.0572 . 
SITE_TEXTC44.9 Skin, NOS                                  7.677e-01  2.155e+00  4.933e-01  1.556   0.1196   
SITE_TEXTC50.0 Nipple                                            NA         NA  0.000e+00     NA       NA   
SITE_TEXTC51.0 Labium majus                              -7.908e-01  4.535e-01  4.537e-01 -1.743   0.0813 . 
SITE_TEXTC51.1 Labium minus                              -6.349e-01  5.300e-01  6.908e-01 -0.919   0.3580   
SITE_TEXTC51.2 Clitoris                                  -1.555e+01  1.760e-07  2.937e+03 -0.005   0.9958   
SITE_TEXTC51.8 Overlapping lesion of vulva               -3.929e-01  6.751e-01  4.536e-01 -0.866   0.3864   
SITE_TEXTC51.9 Vulva, NOS                                -5.073e-01  6.021e-01  3.857e-01 -1.315   0.1884   
SITE_TEXTC52.9 Vagina, NOS                               -1.554e+01  1.787e-07  4.804e+03 -0.003   0.9974   
SITE_TEXTC60.0 Prepuce                                           NA         NA  0.000e+00     NA       NA   
SITE_TEXTC60.1 Glans penis                                       NA         NA  0.000e+00     NA       NA   
SITE_TEXTC60.2 Body of penis                             -1.554e+01  1.776e-07  3.997e+03 -0.004   0.9969   
SITE_TEXTC60.8 Overlapping lesion of penis               -1.556e+01  1.749e-07  2.565e+03 -0.006   0.9952   
SITE_TEXTC60.9 Penis                                             NA         NA  0.000e+00     NA       NA   
SITE_TEXTC63.2 Scrotum, NOS                                      NA         NA  0.000e+00     NA       NA   
SITE_TEXTC77.0 Lymph Nodes: HeadFaceNeck                         NA         NA  0.000e+00     NA       NA   
SITE_TEXTC77.1 Intrathoracic Lymph Nodes                         NA         NA  0.000e+00     NA       NA   
SITE_TEXTC77.2 Intra-abdominal LymphNodes                        NA         NA  0.000e+00     NA       NA   
SITE_TEXTC77.3 Lymph Nodes of axilla or arm                      NA         NA  0.000e+00     NA       NA   
SITE_TEXTC77.4 Lymph Nodes: Leg                                  NA         NA  0.000e+00     NA       NA   
SITE_TEXTC77.5 Pelvic Lymph Nodes                                NA         NA  0.000e+00     NA       NA   
SITE_TEXTC77.8 Lymph Nodes: multiple region                      NA         NA  0.000e+00     NA       NA   
SITE_TEXTC77.9 Lymph Node NOS                                    NA         NA  0.000e+00     NA       NA   
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

                                                         exp(coef) exp(-coef) lower .95 upper .95
SITE_TEXTC00.1 External Lip: Lower NOS                          NA         NA        NA        NA
SITE_TEXTC00.2 External Lip: NOS                                NA         NA        NA        NA
SITE_TEXTC00.3 Lip: Upper Mucosa                                NA         NA        NA        NA
SITE_TEXTC00.4 Lip: Lower Mucosa                                NA         NA        NA        NA
SITE_TEXTC00.5 Lip: Mucosa NOS                                  NA         NA        NA        NA
SITE_TEXTC00.6 Lip: Commissure                                  NA         NA        NA        NA
SITE_TEXTC00.8 Lip: Overlapping                                 NA         NA        NA        NA
SITE_TEXTC00.9 Lip NOS                                          NA         NA        NA        NA
SITE_TEXTC01.9 Tongue: Base NOS                                 NA         NA        NA        NA
SITE_TEXTC02.0 Tongue: Dorsal NOS                               NA         NA        NA        NA
SITE_TEXTC02.1 Tongue: Border, Tip                              NA         NA        NA        NA
SITE_TEXTC02.2 Tongue: Ventral NOS                              NA         NA        NA        NA
SITE_TEXTC02.3 Tongue: Anterior NOS                             NA         NA        NA        NA
SITE_TEXTC02.4 Lingual Tonsil                                   NA         NA        NA        NA
SITE_TEXTC02.8 Tongue: Overlapping                              NA         NA        NA        NA
SITE_TEXTC02.9 Tongue: NOS                                      NA         NA        NA        NA
SITE_TEXTC03.0 Gum: Upper                                       NA         NA        NA        NA
SITE_TEXTC03.1 Gum: Lower                                       NA         NA        NA        NA
SITE_TEXTC03.9 Gum NOS                                          NA         NA        NA        NA
SITE_TEXTC04.0 Mouth: Anterior Floor                            NA         NA        NA        NA
SITE_TEXTC04.1 Mouth: Lateral Floor                             NA         NA        NA        NA
SITE_TEXTC04.9 Floor of Mouth NOS                               NA         NA        NA        NA
SITE_TEXTC05.0 Hard Palate                                      NA         NA        NA        NA
SITE_TEXTC05.1 Soft Palate NOS                                  NA         NA        NA        NA
SITE_TEXTC05.2 Uvula                                            NA         NA        NA        NA
SITE_TEXTC05.8 Palate: Overlapping                              NA         NA        NA        NA
SITE_TEXTC05.9 Palate NOS                                       NA         NA        NA        NA
SITE_TEXTC06.0 Cheek Mucosa                                     NA         NA        NA        NA
SITE_TEXTC06.1 Mouth: Vestibule                                 NA         NA        NA        NA
SITE_TEXTC06.2 Retromolar Area                                  NA         NA        NA        NA
SITE_TEXTC06.8 Mouth: Other Overlapping                         NA         NA        NA        NA
SITE_TEXTC06.9 Mouth NOS                                        NA         NA        NA        NA
SITE_TEXTC07.9 Parotid Gland                                    NA         NA        NA        NA
SITE_TEXTC09.8 Tonsil: Overlapping                              NA         NA        NA        NA
SITE_TEXTC09.9 Tonsil NOS                                       NA         NA        NA        NA
SITE_TEXTC11.1 Nasopharynx: Poster Wall                         NA         NA        NA        NA
SITE_TEXTC14.2 Waldeyer Ring                                    NA         NA        NA        NA
SITE_TEXTC30.0 Nasal Cavity                                     NA         NA        NA        NA
SITE_TEXTC37.9 Thymus                                           NA         NA        NA        NA
SITE_TEXTC42.0 Blood                                            NA         NA        NA        NA
SITE_TEXTC42.2 Spleen                                           NA         NA        NA        NA
SITE_TEXTC42.4 Hematopoietic NOS                                NA         NA        NA        NA
SITE_TEXTC44.0 Skin of lip, NOS                                 NA         NA        NA        NA
SITE_TEXTC44.1 Eyelid                                           NA         NA        NA        NA
SITE_TEXTC44.2 External ear                              1.740e-07  5.749e+06    0.0000       Inf
SITE_TEXTC44.3 Skin of ear and unspecified parts of face 2.482e+01  4.029e-02    2.9546   208.468
SITE_TEXTC44.4 Skin of scalp and neck                    1.769e-07  5.652e+06    0.0000       Inf
SITE_TEXTC44.5 Skin of trunk                             1.056e+00  9.469e-01    0.4887     2.282
SITE_TEXTC44.6 Skin of upper limb and shoulder           1.326e+00  7.539e-01    0.2754     6.388
SITE_TEXTC44.7 Skin of lower limb and hip                1.732e+00  5.772e-01    0.5495     5.462
SITE_TEXTC44.8 Overlapping lesion of skin                2.766e+00  3.616e-01    0.9694     7.889
SITE_TEXTC44.9 Skin, NOS                                 2.155e+00  4.641e-01    0.8195     5.666
SITE_TEXTC50.0 Nipple                                           NA         NA        NA        NA
SITE_TEXTC51.0 Labium majus                              4.535e-01  2.205e+00    0.1864     1.103
SITE_TEXTC51.1 Labium minus                              5.300e-01  1.887e+00    0.1369     2.052
SITE_TEXTC51.2 Clitoris                                  1.760e-07  5.683e+06    0.0000       Inf
SITE_TEXTC51.8 Overlapping lesion of vulva               6.751e-01  1.481e+00    0.2775     1.642
SITE_TEXTC51.9 Vulva, NOS                                6.021e-01  1.661e+00    0.2828     1.282
SITE_TEXTC52.9 Vagina, NOS                               1.787e-07  5.596e+06    0.0000       Inf
SITE_TEXTC60.0 Prepuce                                          NA         NA        NA        NA
SITE_TEXTC60.1 Glans penis                                      NA         NA        NA        NA
SITE_TEXTC60.2 Body of penis                             1.776e-07  5.630e+06    0.0000       Inf
SITE_TEXTC60.8 Overlapping lesion of penis               1.749e-07  5.719e+06    0.0000       Inf
SITE_TEXTC60.9 Penis                                            NA         NA        NA        NA
SITE_TEXTC63.2 Scrotum, NOS                                     NA         NA        NA        NA
SITE_TEXTC77.0 Lymph Nodes: HeadFaceNeck                        NA         NA        NA        NA
SITE_TEXTC77.1 Intrathoracic Lymph Nodes                        NA         NA        NA        NA
SITE_TEXTC77.2 Intra-abdominal LymphNodes                       NA         NA        NA        NA
SITE_TEXTC77.3 Lymph Nodes of axilla or arm                     NA         NA        NA        NA
SITE_TEXTC77.4 Lymph Nodes: Leg                                 NA         NA        NA        NA
SITE_TEXTC77.5 Pelvic Lymph Nodes                               NA         NA        NA        NA
SITE_TEXTC77.8 Lymph Nodes: multiple region                     NA         NA        NA        NA
SITE_TEXTC77.9 Lymph Node NOS                                   NA         NA        NA        NA

Concordance= 0.593  (se = 0.015 )
Rsquare= 0.039   (max possible= 0.956 )
Likelihood ratio test= 53.58  on 16 df,   p=6.07e-06
Wald test            = 61.46  on 16 df,   p=2.971e-07
Score (logrank) test = 88.8  on 16 df,   p=4.164e-12
Transformation introduced infinite values in continuous y-axisTransformation introduced infinite values in continuous y-axisTransformation introduced infinite values in continuous y-axisRemoved 58 rows containing missing values (geom_errorbar).Removed 74 rows containing missing values (geom_text).Removed 74 rows containing missing values (geom_text).Removed 74 rows containing missing values (geom_text).Removed 74 rows containing missing values (geom_text).Removed 74 rows containing missing values (geom_text).Removed 1 rows containing missing values (geom_text).



   
## Unadjusted Kaplan Meier Overall Survival Curve for:  SITE_TEXT
This manual palette can handle a maximum of 10 values. You have supplied 17.

Histology

#uni_var(test_var = "HISTOLOGY_F_LIM", data_imp = data)

Grade

uni_var(test_var = "GRADE_F", data_imp = data)
_________________________________________________
   
## GRADE_F
_________________________________________________
Call: survfit(formula = Surv(DX_LASTCONTACT_DEATH_MONTHS, PUF_VITAL_STATUS == 
    0) ~ GRADE_F, data = data)

                             n events median 0.95LCL 0.95UCL
GRADE_F=Gr I: Well Diff     38     10  131.9    70.3      NA
GRADE_F=Gr II: Mod Diff     30     15   67.1    51.4      NA
GRADE_F=Gr III: Poor Diff   36     23   29.3    21.6      NA
GRADE_F=NA/Unkown         1256    281  155.4   133.1      NA

Call: survfit(formula = Surv(DX_LASTCONTACT_DEATH_MONTHS, PUF_VITAL_STATUS == 
    0) ~ GRADE_F, data = data)

                GRADE_F=Gr I: Well Diff 
 time n.risk n.event survival std.err lower 95% CI upper 95% CI
   12     37       0    1.000  0.0000        1.000         1.00
   24     34       2    0.945  0.0377        0.874         1.00
   36     30       1    0.916  0.0467        0.829         1.00
   48     20       0    0.916  0.0467        0.829         1.00
   60     15       3    0.771  0.0861        0.620         0.96
  120      4       3    0.591  0.1131        0.406         0.86

                GRADE_F=Gr II: Mod Diff 
 time n.risk n.event survival std.err lower 95% CI upper 95% CI
   12     25       3    0.893  0.0585        0.785        1.000
   24     18       5    0.709  0.0867        0.558        0.901
   36     15       0    0.709  0.0867        0.558        0.901
   48     14       1    0.662  0.0929        0.503        0.872
   60     10       3    0.516  0.1041        0.347        0.766
  120      1       3    0.199  0.1510        0.045        0.880

                GRADE_F=Gr III: Poor Diff 
 time n.risk n.event survival std.err lower 95% CI upper 95% CI
   12     28       4    0.884  0.0548       0.7826        0.998
   24     15      10    0.537  0.0920       0.3843        0.752
   36     10       4    0.384  0.0924       0.2396        0.615
   48      9       1    0.345  0.0907       0.2065        0.578
   60      8       1    0.307  0.0884       0.1747        0.540
  120      1       2    0.134  0.1041       0.0294        0.614

                GRADE_F=NA/Unkown 
 time n.risk n.event survival std.err lower 95% CI upper 95% CI
   12   1094      41    0.965 0.00530        0.955        0.976
   24    965      35    0.933 0.00742        0.919        0.948
   36    809      35    0.897 0.00935        0.879        0.915
   48    681      32    0.859 0.01108        0.838        0.881
   60    559      37    0.809 0.01315        0.784        0.835
  120    119      87    0.607 0.02270        0.564        0.653




   
## Univariable Cox Proportional Hazard Model for:  GRADE_F
X matrix deemed to be singular; variable 3 4 5 6 7
Call:
coxph(formula = Surv(DX_LASTCONTACT_DEATH_MONTHS, PUF_VITAL_STATUS == 
    0) ~ GRADE_F, data = data)

  n= 1360, number of events= 329 

                                   coef exp(coef) se(coef)      z Pr(>|z|)    
GRADE_FGr II: Mod Diff           0.8652    2.3754   0.4085  2.118 0.034169 *  
GRADE_FGr III: Poor Diff         1.4655    4.3296   0.3794  3.863 0.000112 ***
GRADE_FGr IV: Undiff/Anaplastic      NA        NA   0.0000     NA       NA    
GRADE_F5                             NA        NA   0.0000     NA       NA    
GRADE_F6                             NA        NA   0.0000     NA       NA    
GRADE_F7                             NA        NA   0.0000     NA       NA    
GRADE_F8                             NA        NA   0.0000     NA       NA    
GRADE_FNA/Unkown                -0.1716    0.8423   0.3220 -0.533 0.594005    
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

                                exp(coef) exp(-coef) lower .95 upper .95
GRADE_FGr II: Mod Diff             2.3754      0.421    1.0667     5.290
GRADE_FGr III: Poor Diff           4.3296      0.231    2.0583     9.107
GRADE_FGr IV: Undiff/Anaplastic        NA         NA        NA        NA
GRADE_F5                               NA         NA        NA        NA
GRADE_F6                               NA         NA        NA        NA
GRADE_F7                               NA         NA        NA        NA
GRADE_F8                               NA         NA        NA        NA
GRADE_FNA/Unkown                   0.8423      1.187    0.4481     1.583

Concordance= 0.553  (se = 0.008 )
Rsquare= 0.034   (max possible= 0.956 )
Likelihood ratio test= 47.18  on 3 df,   p=3.183e-10
Wald test            = 68.17  on 3 df,   p=1.055e-14
Score (logrank) test = 82.42  on 3 df,   p=0
Removed 6 rows containing missing values (geom_errorbar).



   
## Unadjusted Kaplan Meier Overall Survival Curve for:  GRADE_F

Clinical T Stage

#uni_var(test_var = "TNM_CLIN_T", data_imp = data)

Clinical N Stage

#uni_var(test_var = "TNM_CLIN_N", data_imp = data)

Clinical M Stage

#uni_var(test_var = "TNM_CLIN_M", data_imp = data)

Clinical Stage Group

uni_var(test_var = "TNM_CLIN_STAGE_GROUP", data_imp = data)
_________________________________________________
   
## TNM_CLIN_STAGE_GROUP
_________________________________________________
Call: survfit(formula = Surv(DX_LASTCONTACT_DEATH_MONTHS, PUF_VITAL_STATUS == 
    0) ~ TNM_CLIN_STAGE_GROUP, data = data)

                           n events median 0.95LCL 0.95UCL
TNM_CLIN_STAGE_GROUP=0    64     14  155.4  114.99      NA
TNM_CLIN_STAGE_GROUP=1   196     35     NA  103.66      NA
TNM_CLIN_STAGE_GROUP=1A  109     14     NA      NA      NA
TNM_CLIN_STAGE_GROUP=1B  119     19     NA      NA      NA
TNM_CLIN_STAGE_GROUP=2   184     51  121.8   97.25      NA
TNM_CLIN_STAGE_GROUP=3    15      4     NA  110.72      NA
TNM_CLIN_STAGE_GROUP=3B    1      1   20.9      NA      NA
TNM_CLIN_STAGE_GROUP=4     9      7   22.2   10.81      NA
TNM_CLIN_STAGE_GROUP=4A    2      0     NA      NA      NA
TNM_CLIN_STAGE_GROUP=4B    5      5   12.9    6.67      NA
TNM_CLIN_STAGE_GROUP=N_A   1      0     NA      NA      NA
TNM_CLIN_STAGE_GROUP=99  655    179  133.4  126.09      NA

Call: survfit(formula = Surv(DX_LASTCONTACT_DEATH_MONTHS, PUF_VITAL_STATUS == 
    0) ~ TNM_CLIN_STAGE_GROUP, data = data)

                TNM_CLIN_STAGE_GROUP=0 
 time n.risk n.event survival std.err lower 95% CI upper 95% CI
   12     57       2    0.966  0.0238        0.920        1.000
   24     49       2    0.930  0.0336        0.867        0.999
   36     48       1    0.911  0.0379        0.840        0.989
   48     39       0    0.911  0.0379        0.840        0.989
   60     31       3    0.835  0.0546        0.735        0.949
  120      7       5    0.612  0.1067        0.435        0.861

                TNM_CLIN_STAGE_GROUP=1 
 time n.risk n.event survival std.err lower 95% CI upper 95% CI
   12    169       7    0.962  0.0140        0.935        0.990
   24    151       4    0.939  0.0180        0.904        0.974
   36    126       4    0.911  0.0220        0.869        0.956
   48    105       4    0.882  0.0258        0.832        0.934
   60     85       5    0.836  0.0317        0.776        0.900
  120     11      11    0.610  0.0665        0.492        0.755

                TNM_CLIN_STAGE_GROUP=1A 
 time n.risk n.event survival std.err lower 95% CI upper 95% CI
   12     93       2    0.979  0.0144        0.951        1.000
   24     82       4    0.937  0.0251        0.889        0.987
   36     69       2    0.912  0.0297        0.856        0.972
   48     53       0    0.912  0.0297        0.856        0.972
   60     37       4    0.836  0.0457        0.751        0.930
  120      9       1    0.812  0.0503        0.719        0.917

                TNM_CLIN_STAGE_GROUP=1B 
 time n.risk n.event survival std.err lower 95% CI upper 95% CI
   12    105       3    0.974  0.0146        0.946        1.000
   24     92       5    0.926  0.0253        0.877        0.977
   36     66       4    0.879  0.0332        0.816        0.947
   48     54       1    0.864  0.0359        0.797        0.937
   60     42       1    0.847  0.0391        0.774        0.927
  120      3       5    0.703  0.0691        0.580        0.852

                TNM_CLIN_STAGE_GROUP=2 
 time n.risk n.event survival std.err lower 95% CI upper 95% CI
   12    163       4    0.977  0.0115        0.954        1.000
   24    139       8    0.926  0.0206        0.887        0.967
   36    115       6    0.883  0.0260        0.834        0.936
   48     96       5    0.841  0.0309        0.782        0.904
   60     82       8    0.768  0.0375        0.698        0.845
  120     13      16    0.538  0.0576        0.436        0.664

                TNM_CLIN_STAGE_GROUP=3 
 time n.risk n.event survival std.err lower 95% CI upper 95% CI
   12     13       2    0.867  0.0878        0.711            1
   24     13       0    0.867  0.0878        0.711            1
   36     12       1    0.800  0.1033        0.621            1
   48     12       0    0.800  0.1033        0.621            1
   60     12       0    0.800  0.1033        0.621            1
  120      1       1    0.533  0.2284        0.230            1

                TNM_CLIN_STAGE_GROUP=3B 
        time       n.risk      n.event     survival      std.err lower 95% CI upper 95% CI 
          12            1            0            1            0            1            1 

                TNM_CLIN_STAGE_GROUP=4 
 time n.risk n.event survival std.err lower 95% CI upper 95% CI
   12      6       3    0.667   0.157       0.4200        1.000
   24      3       3    0.333   0.157       0.1323        0.840
   36      2       1    0.222   0.139       0.0655        0.754
   48      1       0    0.222   0.139       0.0655        0.754
   60      1       0    0.222   0.139       0.0655        0.754

                TNM_CLIN_STAGE_GROUP=4A 
 time n.risk n.event survival std.err lower 95% CI upper 95% CI
   12      2       0        1       0            1            1
   24      1       0        1       0            1            1
   36      1       0        1       0            1            1

                TNM_CLIN_STAGE_GROUP=4B 
        time       n.risk      n.event     survival      std.err lower 95% CI upper 95% CI 
      12.000        3.000        2.000        0.600        0.219        0.293        1.000 

                TNM_CLIN_STAGE_GROUP=N_A 
     time n.risk n.event survival std.err lower 95% CI upper 95% CI

                TNM_CLIN_STAGE_GROUP=99 
 time n.risk n.event survival std.err lower 95% CI upper 95% CI
   12    572      23    0.963 0.00749        0.949        0.978
   24    502      22    0.925 0.01084        0.904        0.946
   36    425      21    0.883 0.01363        0.857        0.910
   48    364      24    0.831 0.01649        0.799        0.864
   60    302      23    0.775 0.01904        0.739        0.813
  120     81      56    0.571 0.02868        0.518        0.630




   
## Univariable Cox Proportional Hazard Model for:  TNM_CLIN_STAGE_GROUP
Loglik converged before variable  14,19 ; beta may be infinite. X matrix deemed to be singular; variable 4 6 7 8 10 12 15 16 18
Call:
coxph(formula = Surv(DX_LASTCONTACT_DEATH_MONTHS, PUF_VITAL_STATUS == 
    0) ~ TNM_CLIN_STAGE_GROUP, data = data)

  n= 1360, number of events= 329 

                              coef  exp(coef)   se(coef)      z Pr(>|z|)    
TNM_CLIN_STAGE_GROUP1   -1.658e-02  9.836e-01  3.165e-01 -0.052  0.95823    
TNM_CLIN_STAGE_GROUP1A  -2.947e-01  7.447e-01  3.781e-01 -0.779  0.43573    
TNM_CLIN_STAGE_GROUP1B   6.078e-02  1.063e+00  3.531e-01  0.172  0.86333    
TNM_CLIN_STAGE_GROUP1C          NA         NA  0.000e+00     NA       NA    
TNM_CLIN_STAGE_GROUP2    3.647e-01  1.440e+00  3.018e-01  1.208  0.22691    
TNM_CLIN_STAGE_GROUP2A          NA         NA  0.000e+00     NA       NA    
TNM_CLIN_STAGE_GROUP2B          NA         NA  0.000e+00     NA       NA    
TNM_CLIN_STAGE_GROUP2C          NA         NA  0.000e+00     NA       NA    
TNM_CLIN_STAGE_GROUP3   -5.652e-02  9.450e-01  5.674e-01 -0.100  0.92064    
TNM_CLIN_STAGE_GROUP3A          NA         NA  0.000e+00     NA       NA    
TNM_CLIN_STAGE_GROUP3B   2.925e+00  1.863e+01  1.040e+00  2.812  0.00492 ** 
TNM_CLIN_STAGE_GROUP3C          NA         NA  0.000e+00     NA       NA    
TNM_CLIN_STAGE_GROUP4    2.323e+00  1.020e+01  4.653e-01  4.992 5.99e-07 ***
TNM_CLIN_STAGE_GROUP4A  -1.244e+01  3.959e-06  1.258e+03 -0.010  0.99211    
TNM_CLIN_STAGE_GROUP4A1         NA         NA  0.000e+00     NA       NA    
TNM_CLIN_STAGE_GROUP4A2         NA         NA  0.000e+00     NA       NA    
TNM_CLIN_STAGE_GROUP4B   3.690e+00  4.003e+01  5.367e-01  6.875 6.19e-12 ***
TNM_CLIN_STAGE_GROUP4C          NA         NA  0.000e+00     NA       NA    
TNM_CLIN_STAGE_GROUPN_A -1.243e+01  4.010e-06  7.660e+03 -0.002  0.99871    
TNM_CLIN_STAGE_GROUP99   2.520e-01  1.287e+00  2.776e-01  0.908  0.36398    
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

                        exp(coef) exp(-coef) lower .95 upper .95
TNM_CLIN_STAGE_GROUP1   9.836e-01  1.017e+00    0.5289     1.829
TNM_CLIN_STAGE_GROUP1A  7.447e-01  1.343e+00    0.3549     1.563
TNM_CLIN_STAGE_GROUP1B  1.063e+00  9.410e-01    0.5319     2.123
TNM_CLIN_STAGE_GROUP1C         NA         NA        NA        NA
TNM_CLIN_STAGE_GROUP2   1.440e+00  6.944e-01    0.7970     2.602
TNM_CLIN_STAGE_GROUP2A         NA         NA        NA        NA
TNM_CLIN_STAGE_GROUP2B         NA         NA        NA        NA
TNM_CLIN_STAGE_GROUP2C         NA         NA        NA        NA
TNM_CLIN_STAGE_GROUP3   9.450e-01  1.058e+00    0.3108     2.873
TNM_CLIN_STAGE_GROUP3A         NA         NA        NA        NA
TNM_CLIN_STAGE_GROUP3B  1.863e+01  5.366e-02    2.4263   143.117
TNM_CLIN_STAGE_GROUP3C         NA         NA        NA        NA
TNM_CLIN_STAGE_GROUP4   1.020e+01  9.802e-02    4.0985    25.396
TNM_CLIN_STAGE_GROUP4A  3.959e-06  2.526e+05    0.0000       Inf
TNM_CLIN_STAGE_GROUP4A1        NA         NA        NA        NA
TNM_CLIN_STAGE_GROUP4A2        NA         NA        NA        NA
TNM_CLIN_STAGE_GROUP4B  4.003e+01  2.498e-02   13.9819   114.594
TNM_CLIN_STAGE_GROUP4C         NA         NA        NA        NA
TNM_CLIN_STAGE_GROUPN_A 4.010e-06  2.494e+05    0.0000       Inf
TNM_CLIN_STAGE_GROUP99  1.287e+00  7.773e-01    0.7467     2.217

Concordance= 0.56  (se = 0.017 )
Rsquare= 0.039   (max possible= 0.956 )
Likelihood ratio test= 54.76  on 11 df,   p=8.583e-08
Wald test            = 97.79  on 11 df,   p=4.441e-16
Score (logrank) test = 201.6  on 11 df,   p=0
Transformation introduced infinite values in continuous y-axisTransformation introduced infinite values in continuous y-axisTransformation introduced infinite values in continuous y-axisRemoved 10 rows containing missing values (geom_errorbar).Removed 21 rows containing missing values (geom_text).Removed 21 rows containing missing values (geom_text).Removed 21 rows containing missing values (geom_text).Removed 21 rows containing missing values (geom_text).Removed 21 rows containing missing values (geom_text).Removed 1 rows containing missing values (geom_text).



   
## Unadjusted Kaplan Meier Overall Survival Curve for:  TNM_CLIN_STAGE_GROUP
This manual palette can handle a maximum of 10 values. You have supplied 12.

Pathologic T Stage

#uni_var(test_var = "TNM_PATH_T", data_imp = data)

Pathologic N Stage

#uni_var(test_var = "TNM_PATH_N", data_imp = data)

Pathologic M Stage

#uni_var(test_var = "TNM_PATH_M", data_imp = data)

Pathologic Stage Group

uni_var(test_var = "TNM_PATH_STAGE_GROUP", data_imp = data)
_________________________________________________
   
## TNM_PATH_STAGE_GROUP
_________________________________________________
Call: survfit(formula = Surv(DX_LASTCONTACT_DEATH_MONTHS, PUF_VITAL_STATUS == 
    0) ~ TNM_PATH_STAGE_GROUP, data = data)

   79 observations deleted due to missingness 
                           n events median 0.95LCL 0.95UCL
TNM_PATH_STAGE_GROUP=0    58     12     NA  128.46      NA
TNM_PATH_STAGE_GROUP=1   107     18     NA  120.94      NA
TNM_PATH_STAGE_GROUP=1A  104     18  155.4  114.99      NA
TNM_PATH_STAGE_GROUP=1B   92     13     NA      NA      NA
TNM_PATH_STAGE_GROUP=2   120     24     NA  130.53      NA
TNM_PATH_STAGE_GROUP=3    26     14  110.7   49.74      NA
TNM_PATH_STAGE_GROUP=3A    2      2   20.6    2.04      NA
TNM_PATH_STAGE_GROUP=3C    5      4   14.6   11.17      NA
TNM_PATH_STAGE_GROUP=4     4      2   23.6    5.06      NA
TNM_PATH_STAGE_GROUP=4A    5      3   16.9    4.34      NA
TNM_PATH_STAGE_GROUP=4B    1      1   13.5      NA      NA
TNM_PATH_STAGE_GROUP=N_A   1      0     NA      NA      NA
TNM_PATH_STAGE_GROUP=99  756    204  133.1  115.94      NA

Call: survfit(formula = Surv(DX_LASTCONTACT_DEATH_MONTHS, PUF_VITAL_STATUS == 
    0) ~ TNM_PATH_STAGE_GROUP, data = data)

79 observations deleted due to missingness 
                TNM_PATH_STAGE_GROUP=0 
 time n.risk n.event survival std.err lower 95% CI upper 95% CI
   12     52       1    0.981  0.0190        0.944        1.000
   24     46       1    0.961  0.0269        0.910        1.000
   36     43       1    0.939  0.0340        0.875        1.000
   48     39       0    0.939  0.0340        0.875        1.000
   60     33       2    0.888  0.0477        0.800        0.987
  120     10       6    0.694  0.0806        0.553        0.872

                TNM_PATH_STAGE_GROUP=1 
 time n.risk n.event survival std.err lower 95% CI upper 95% CI
   12     92       1    0.990 0.00985        0.971        1.000
   24     84       1    0.979 0.01503        0.950        1.000
   36     76       1    0.966 0.01948        0.928        1.000
   48     66       2    0.940 0.02613        0.890        0.993
   60     54       4    0.879 0.03833        0.807        0.958
  120     11       7    0.668 0.08129        0.526        0.848

                TNM_PATH_STAGE_GROUP=1A 
 time n.risk n.event survival std.err lower 95% CI upper 95% CI
   12     97       1    0.990  0.0100        0.970        1.000
   24     87       3    0.958  0.0206        0.918        0.999
   36     77       3    0.923  0.0280        0.870        0.980
   48     58       2    0.896  0.0331        0.834        0.963
   60     45       3    0.844  0.0426        0.765        0.932
  120      9       5    0.652  0.0895        0.498        0.853

                TNM_PATH_STAGE_GROUP=1B 
 time n.risk n.event survival std.err lower 95% CI upper 95% CI
   12     78       2    0.977  0.0161        0.946        1.000
   24     69       1    0.963  0.0208        0.924        1.000
   36     52       2    0.931  0.0302        0.874        0.992
   48     41       3    0.870  0.0444        0.787        0.961
   60     34       2    0.823  0.0530        0.725        0.934
  120      2       3    0.723  0.0731        0.593        0.881

                TNM_PATH_STAGE_GROUP=2 
 time n.risk n.event survival std.err lower 95% CI upper 95% CI
   12    107       1    0.991 0.00897        0.974        1.000
   24     93       5    0.942 0.02315        0.897        0.988
   36     83       2    0.920 0.02704        0.869        0.975
   48     72       3    0.884 0.03328        0.821        0.951
   60     62       2    0.858 0.03706        0.788        0.933
  120     19       8    0.720 0.05499        0.620        0.836

                TNM_PATH_STAGE_GROUP=3 
 time n.risk n.event survival std.err lower 95% CI upper 95% CI
   12     23       3    0.885  0.0627        0.770        1.000
   24     21       2    0.808  0.0773        0.670        0.974
   36     21       0    0.808  0.0773        0.670        0.974
   48     18       3    0.692  0.0905        0.536        0.895
   60     17       1    0.654  0.0933        0.494        0.865
  120      2       4    0.294  0.1448        0.112        0.772

                TNM_PATH_STAGE_GROUP=3A 
 time n.risk n.event survival std.err lower 95% CI upper 95% CI
   12      1       1      0.5   0.354        0.125            1
   24      1       0      0.5   0.354        0.125            1
   36      1       0      0.5   0.354        0.125            1

                TNM_PATH_STAGE_GROUP=3C 
        time       n.risk      n.event     survival      std.err lower 95% CI upper 95% CI 
      12.000        3.000        2.000        0.600        0.219        0.293        1.000 

                TNM_PATH_STAGE_GROUP=4 
 time n.risk n.event survival std.err lower 95% CI upper 95% CI
   12      3       1     0.75   0.217        0.426            1
   24      2       1     0.50   0.250        0.188            1

                TNM_PATH_STAGE_GROUP=4A 
 time n.risk n.event survival std.err lower 95% CI upper 95% CI
   12      3       2      0.6   0.219        0.293            1
   24      2       1      0.4   0.219        0.137            1
   36      2       0      0.4   0.219        0.137            1
   48      1       0      0.4   0.219        0.137            1
   60      1       0      0.4   0.219        0.137            1

                TNM_PATH_STAGE_GROUP=4B 
        time       n.risk      n.event     survival      std.err lower 95% CI upper 95% CI 
          12            1            0            1            0            1            1 

                TNM_PATH_STAGE_GROUP=N_A 
     time n.risk n.event survival std.err lower 95% CI upper 95% CI

                TNM_PATH_STAGE_GROUP=99 
 time n.risk n.event survival std.err lower 95% CI upper 95% CI
   12    659      30    0.958 0.00748        0.944        0.973
   24    572      29    0.915 0.01066        0.894        0.936
   36    475      29    0.865 0.01353        0.839        0.892
   48    402      19    0.828 0.01531        0.799        0.859
   60    330      29    0.765 0.01809        0.731        0.802
  120     72      60    0.552 0.02880        0.499        0.612




   
## Univariable Cox Proportional Hazard Model for:  TNM_PATH_STAGE_GROUP
Loglik converged before variable  18 ; beta may be infinite. X matrix deemed to be singular; variable 4 6 7 8 11 15 17
Call:
coxph(formula = Surv(DX_LASTCONTACT_DEATH_MONTHS, PUF_VITAL_STATUS == 
    0) ~ TNM_PATH_STAGE_GROUP, data = data)

  n= 1281, number of events= 315 
   (79 observations deleted due to missingness)

                              coef  exp(coef)   se(coef)      z Pr(>|z|)    
TNM_PATH_STAGE_GROUP1    4.124e-02  1.042e+00  3.731e-01  0.111 0.911974    
TNM_PATH_STAGE_GROUP1A   6.457e-02  1.067e+00  3.732e-01  0.173 0.862617    
TNM_PATH_STAGE_GROUP1B   1.515e-01  1.164e+00  4.016e-01  0.377 0.706068    
TNM_PATH_STAGE_GROUP1C          NA         NA  0.000e+00     NA       NA    
TNM_PATH_STAGE_GROUP2    5.139e-02  1.053e+00  3.536e-01  0.145 0.884434    
TNM_PATH_STAGE_GROUP2A          NA         NA  0.000e+00     NA       NA    
TNM_PATH_STAGE_GROUP2B          NA         NA  0.000e+00     NA       NA    
TNM_PATH_STAGE_GROUP2C          NA         NA  0.000e+00     NA       NA    
TNM_PATH_STAGE_GROUP3    9.321e-01  2.540e+00  3.937e-01  2.368 0.017900 *  
TNM_PATH_STAGE_GROUP3A   3.075e+00  2.165e+01  7.676e-01  4.006 6.18e-05 ***
TNM_PATH_STAGE_GROUP3B          NA         NA  0.000e+00     NA       NA    
TNM_PATH_STAGE_GROUP3C   3.388e+00  2.961e+01  5.896e-01  5.746 9.12e-09 ***
TNM_PATH_STAGE_GROUP4    2.273e+00  9.710e+00  7.682e-01  2.959 0.003086 ** 
TNM_PATH_STAGE_GROUP4A   1.857e+00  6.403e+00  6.462e-01  2.874 0.004058 ** 
TNM_PATH_STAGE_GROUP4A1         NA         NA  0.000e+00     NA       NA    
TNM_PATH_STAGE_GROUP4B   3.622e+00  3.743e+01  1.050e+00  3.450 0.000561 ***
TNM_PATH_STAGE_GROUP4C          NA         NA  0.000e+00     NA       NA    
TNM_PATH_STAGE_GROUPN_A -8.254e+00  2.602e-04  1.082e+03 -0.008 0.993915    
TNM_PATH_STAGE_GROUP99   5.196e-01  1.681e+00  2.973e-01  1.748 0.080538 .  
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

                        exp(coef) exp(-coef) lower .95 upper .95
TNM_PATH_STAGE_GROUP1   1.042e+00  9.596e-01    0.5016     2.165
TNM_PATH_STAGE_GROUP1A  1.067e+00  9.375e-01    0.5134     2.217
TNM_PATH_STAGE_GROUP1B  1.164e+00  8.595e-01    0.5296     2.556
TNM_PATH_STAGE_GROUP1C         NA         NA        NA        NA
TNM_PATH_STAGE_GROUP2   1.053e+00  9.499e-01    0.5264     2.105
TNM_PATH_STAGE_GROUP2A         NA         NA        NA        NA
TNM_PATH_STAGE_GROUP2B         NA         NA        NA        NA
TNM_PATH_STAGE_GROUP2C         NA         NA        NA        NA
TNM_PATH_STAGE_GROUP3   2.540e+00  3.937e-01    1.1741     5.494
TNM_PATH_STAGE_GROUP3A  2.165e+01  4.619e-02    4.8088    97.458
TNM_PATH_STAGE_GROUP3B         NA         NA        NA        NA
TNM_PATH_STAGE_GROUP3C  2.961e+01  3.377e-02    9.3239    94.054
TNM_PATH_STAGE_GROUP4   9.710e+00  1.030e-01    2.1544    43.765
TNM_PATH_STAGE_GROUP4A  6.403e+00  1.562e-01    1.8046    22.719
TNM_PATH_STAGE_GROUP4A1        NA         NA        NA        NA
TNM_PATH_STAGE_GROUP4B  3.743e+01  2.672e-02    4.7798   293.077
TNM_PATH_STAGE_GROUP4C         NA         NA        NA        NA
TNM_PATH_STAGE_GROUPN_A 2.602e-04  3.843e+03    0.0000       Inf
TNM_PATH_STAGE_GROUP99  1.681e+00  5.948e-01    0.9388     3.011

Concordance= 0.596  (se = 0.016 )
Rsquare= 0.04   (max possible= 0.957 )
Likelihood ratio test= 52.84  on 12 df,   p=4.392e-07
Wald test            = 83.27  on 12 df,   p=9.798e-13
Score (logrank) test = 142.6  on 12 df,   p=0
Transformation introduced infinite values in continuous y-axisTransformation introduced infinite values in continuous y-axisTransformation introduced infinite values in continuous y-axisRemoved 8 rows containing missing values (geom_errorbar).Removed 20 rows containing missing values (geom_text).Removed 20 rows containing missing values (geom_text).Removed 20 rows containing missing values (geom_text).Removed 20 rows containing missing values (geom_text).Removed 20 rows containing missing values (geom_text).Removed 1 rows containing missing values (geom_text).



   
## Unadjusted Kaplan Meier Overall Survival Curve for:  TNM_PATH_STAGE_GROUP
This manual palette can handle a maximum of 10 values. You have supplied 13.

Margins

uni_var(test_var = "MARGINS", data_imp = data)
_________________________________________________
   
## MARGINS
_________________________________________________
Call: survfit(formula = Surv(DX_LASTCONTACT_DEATH_MONTHS, PUF_VITAL_STATUS == 
    0) ~ MARGINS, data = data)

                            n events median 0.95LCL 0.95UCL
MARGINS=No Residual       601    124     NA   128.5      NA
MARGINS=Residual, NOS     164     38  131.9   121.8      NA
MARGINS=Microscopic Resid 351     71  155.4   133.4      NA
MARGINS=Macroscopic Resid  17      7   71.5    49.7      NA
MARGINS=Not evaluable      22      3     NA    92.3      NA
MARGINS=No surg           161     75   63.2    53.8    75.6
MARGINS=Unknown            44     11     NA    72.4      NA

Call: survfit(formula = Surv(DX_LASTCONTACT_DEATH_MONTHS, PUF_VITAL_STATUS == 
    0) ~ MARGINS, data = data)

                MARGINS=No Residual 
 time n.risk n.event survival std.err lower 95% CI upper 95% CI
   12    524      13    0.977 0.00638        0.964        0.989
   24    472      14    0.950 0.00945        0.931        0.968
   36    398      16    0.915 0.01246        0.891        0.940
   48    344      14    0.881 0.01503        0.852        0.910
   60    285      21    0.823 0.01858        0.787        0.860
  120     62      41    0.619 0.03318        0.557        0.687

                MARGINS=Residual, NOS 
 time n.risk n.event survival std.err lower 95% CI upper 95% CI
   12    148       4    0.975  0.0124        0.951        0.999
   24    125       9    0.912  0.0233        0.868        0.959
   36    107       4    0.881  0.0272        0.829        0.936
   48     88       4    0.846  0.0313        0.787        0.910
   60     70       6    0.784  0.0379        0.713        0.862
  120     13       8    0.633  0.0600        0.526        0.762

                MARGINS=Microscopic Resid 
 time n.risk n.event survival std.err lower 95% CI upper 95% CI
   12    313      11    0.967 0.00972        0.948        0.986
   24    279       7    0.945 0.01273        0.920        0.970
   36    238       9    0.912 0.01623        0.881        0.945
   48    200       8    0.880 0.01931        0.843        0.918
   60    174       7    0.848 0.02207        0.806        0.892
  120     38      24    0.659 0.04124        0.583        0.745

                MARGINS=Macroscopic Resid 
 time n.risk n.event survival std.err lower 95% CI upper 95% CI
   12     14       0    1.000  0.0000        1.000        1.000
   24     12       2    0.857  0.0935        0.692        1.000
   36      9       1    0.771  0.1170        0.573        1.000
   48      9       0    0.771  0.1170        0.573        1.000
   60      5       2    0.579  0.1471        0.351        0.952
  120      2       1    0.463  0.1567        0.238        0.899

                MARGINS=Not evaluable 
 time n.risk n.event survival std.err lower 95% CI upper 95% CI
   12     17       0    1.000  0.0000        1.000            1
   24     16       0    1.000  0.0000        1.000            1
   36     13       0    1.000  0.0000        1.000            1
   48     10       1    0.923  0.0739        0.789            1
   60      8       0    0.923  0.0739        0.789            1
  120      2       2    0.659  0.1662        0.402            1

                MARGINS=No surg 
 time n.risk n.event survival std.err lower 95% CI upper 95% CI
   12    128      19    0.876  0.0267        0.825        0.930
   24     92      19    0.735  0.0372        0.666        0.812
   36     68       9    0.654  0.0418        0.577        0.742
   48     52       5    0.601  0.0447        0.520        0.695
   60     32       6    0.516  0.0502        0.427        0.625
  120      4      15    0.210  0.0605        0.120        0.369

                MARGINS=Unknown 
 time n.risk n.event survival std.err lower 95% CI upper 95% CI
   12     40       1    0.976  0.0235        0.931        1.000
   24     36       1    0.951  0.0342        0.886        1.000
   36     31       1    0.923  0.0431        0.842        1.000
   48     21       2    0.863  0.0573        0.758        0.983
   60     18       2    0.779  0.0767        0.642        0.945
  120      4       4    0.567  0.1076        0.391        0.823




   
## Univariable Cox Proportional Hazard Model for:  MARGINS

Call:
coxph(formula = Surv(DX_LASTCONTACT_DEATH_MONTHS, PUF_VITAL_STATUS == 
    0) ~ MARGINS, data = data)

  n= 1360, number of events= 329 

                             coef exp(coef) se(coef)      z Pr(>|z|)    
MARGINSResidual, NOS      0.21302   1.23740  0.18558  1.148   0.2510    
MARGINSMicroscopic Resid -0.05557   0.94595  0.14886 -0.373   0.7089    
MARGINSMacroscopic Resid  0.88777   2.42971  0.38887  2.283   0.0224 *  
MARGINSNot evaluable     -0.26976   0.76356  0.58437 -0.462   0.6443    
MARGINSNo surg            1.38452   3.99292  0.14815  9.345   <2e-16 ***
MARGINSUnknown            0.26715   1.30624  0.31484  0.849   0.3961    
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

                         exp(coef) exp(-coef) lower .95 upper .95
MARGINSResidual, NOS        1.2374     0.8081    0.8601     1.780
MARGINSMicroscopic Resid    0.9459     1.0571    0.7066     1.266
MARGINSMacroscopic Resid    2.4297     0.4116    1.1338     5.207
MARGINSNot evaluable        0.7636     1.3097    0.2429     2.400
MARGINSNo surg              3.9929     0.2504    2.9866     5.338
MARGINSUnknown              1.3062     0.7656    0.7047     2.421

Concordance= 0.617  (se = 0.017 )
Rsquare= 0.061   (max possible= 0.956 )
Likelihood ratio test= 85.81  on 6 df,   p=2.22e-16
Wald test            = 107.9  on 6 df,   p=0
Score (logrank) test = 124.8  on 6 df,   p=0
Removed 1 rows containing missing values (geom_errorbar).



   
## Unadjusted Kaplan Meier Overall Survival Curve for:  MARGINS

Margins Yes/No

#uni_var(test_var = "MARGINS_YN", data_imp = data)

30 Day Readmission

uni_var(test_var = "READM_HOSP_30_DAYS_F", data_imp = data)
_________________________________________________
   
## READM_HOSP_30_DAYS_F
_________________________________________________
Call: survfit(formula = Surv(DX_LASTCONTACT_DEATH_MONTHS, PUF_VITAL_STATUS == 
    0) ~ READM_HOSP_30_DAYS_F, data = data)

                                              n events median 0.95LCL 0.95UCL
READM_HOSP_30_DAYS_F=No_Surg_or_No_Readmit 1292    305 141.77   128.2      NA
READM_HOSP_30_DAYS_F=Unplan_Readmit_Same     36     15 110.72    59.9      NA
READM_HOSP_30_DAYS_F=Plan_Readmit_Same       15      5  90.28    66.2      NA
READM_HOSP_30_DAYS_F=PlanUnplan_Same          1      1   1.38      NA      NA
READM_HOSP_30_DAYS_F=9                       16      3     NA      NA      NA

Call: survfit(formula = Surv(DX_LASTCONTACT_DEATH_MONTHS, PUF_VITAL_STATUS == 
    0) ~ READM_HOSP_30_DAYS_F, data = data)

                READM_HOSP_30_DAYS_F=No_Surg_or_No_Readmit 
 time n.risk n.event survival std.err lower 95% CI upper 95% CI
   12   1126      44    0.964 0.00533        0.954        0.975
   24    979      47    0.922 0.00789        0.907        0.937
   36    821      37    0.884 0.00969        0.865        0.903
   48    684      33    0.846 0.01131        0.824        0.869
   60    558      40    0.793 0.01335        0.768        0.820
  120    118      88    0.591 0.02269        0.548        0.637

                READM_HOSP_30_DAYS_F=Unplan_Readmit_Same 
 time n.risk n.event survival std.err lower 95% CI upper 95% CI
   12     30       3    0.915  0.0470        0.827        1.000
   24     27       3    0.823  0.0656        0.704        0.963
   36     24       1    0.792  0.0703        0.665        0.942
   48     21       1    0.756  0.0758        0.621        0.920
   60     17       3    0.642  0.0883        0.491        0.841
  120      5       4    0.441  0.1073        0.273        0.710

                READM_HOSP_30_DAYS_F=Plan_Readmit_Same 
 time n.risk n.event survival std.err lower 95% CI upper 95% CI
   12     14       0    1.000  0.0000        1.000            1
   24     13       1    0.929  0.0688        0.803            1
   36      7       2    0.774  0.1152        0.578            1
   48      7       0    0.774  0.1152        0.578            1
   60      6       0    0.774  0.1152        0.578            1

                READM_HOSP_30_DAYS_F=PlanUnplan_Same 
     time n.risk n.event survival std.err lower 95% CI upper 95% CI

                READM_HOSP_30_DAYS_F=9 
 time n.risk n.event survival std.err lower 95% CI upper 95% CI
   12     14       0    1.000  0.0000        1.000            1
   24     13       1    0.929  0.0688        0.803            1
   36     12       0    0.929  0.0688        0.803            1
   48     12       0    0.929  0.0688        0.803            1
   60     11       1    0.851  0.0973        0.680            1
  120      2       1    0.766  0.1191        0.565            1




   
## Univariable Cox Proportional Hazard Model for:  READM_HOSP_30_DAYS_F

Call:
coxph(formula = Surv(DX_LASTCONTACT_DEATH_MONTHS, PUF_VITAL_STATUS == 
    0) ~ READM_HOSP_30_DAYS_F, data = data)

  n= 1360, number of events= 329 

                                            coef exp(coef) se(coef)      z Pr(>|z|)    
READM_HOSP_30_DAYS_FUnplan_Readmit_Same   0.4686    1.5977   0.2646  1.771   0.0766 .  
READM_HOSP_30_DAYS_FPlan_Readmit_Same     0.4202    1.5223   0.4511  0.931   0.3516    
READM_HOSP_30_DAYS_FPlanUnplan_Same       5.2765  195.6763   1.0691  4.935 8.01e-07 ***
READM_HOSP_30_DAYS_F9                    -0.5392    0.5832   0.5804 -0.929   0.3529    
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

                                        exp(coef) exp(-coef) lower .95 upper .95
READM_HOSP_30_DAYS_FUnplan_Readmit_Same    1.5977    0.62589    0.9512     2.684
READM_HOSP_30_DAYS_FPlan_Readmit_Same      1.5223    0.65690    0.6288     3.686
READM_HOSP_30_DAYS_FPlanUnplan_Same      195.6763    0.00511   24.0705  1590.712
READM_HOSP_30_DAYS_F9                      0.5832    1.71469    0.1870     1.819

Concordance= 0.518  (se = 0.007 )
Rsquare= 0.009   (max possible= 0.956 )
Likelihood ratio test= 12.97  on 4 df,   p=0.01143
Wald test            = 29.02  on 4 df,   p=7.732e-06
Score (logrank) test = 171.6  on 4 df,   p=0
Removed 1 rows containing missing values (geom_errorbar).



   
## Unadjusted Kaplan Meier Overall Survival Curve for:  READM_HOSP_30_DAYS_F

Radiation Type

uni_var(test_var = "RX_SUMM_RADIATION_F", data_imp = data)
_________________________________________________
   
## RX_SUMM_RADIATION_F
_________________________________________________
Call: survfit(formula = Surv(DX_LASTCONTACT_DEATH_MONTHS, PUF_VITAL_STATUS == 
    0) ~ RX_SUMM_RADIATION_F, data = data)

                                              n events median 0.95LCL 0.95UCL
RX_SUMM_RADIATION_F=None                   1268    290  149.6   128.5      NA
RX_SUMM_RADIATION_F=Beam Radiation           73     35   61.9    40.6      NA
RX_SUMM_RADIATION_F=Radioactive Implants      1      0     NA      NA      NA
RX_SUMM_RADIATION_F=Beam + Imp or Isotopes    1      0     NA      NA      NA
RX_SUMM_RADIATION_F=Unknown                  17      4     NA   131.9      NA

Call: survfit(formula = Surv(DX_LASTCONTACT_DEATH_MONTHS, PUF_VITAL_STATUS == 
    0) ~ RX_SUMM_RADIATION_F, data = data)

                RX_SUMM_RADIATION_F=None 
 time n.risk n.event survival std.err lower 95% CI upper 95% CI
   12   1104      39    0.968 0.00512        0.958        0.978
   24    973      40    0.931 0.00752        0.916        0.946
   36    817      37    0.893 0.00947        0.874        0.912
   48    685      31    0.857 0.01110        0.835        0.879
   60    560      41    0.802 0.01330        0.776        0.828
  120    115      88    0.595 0.02306        0.551        0.642

                RX_SUMM_RADIATION_F=Beam Radiation 
 time n.risk n.event survival std.err lower 95% CI upper 95% CI
   12     64       8    0.890  0.0366        0.822        0.965
   24     44      12    0.711  0.0548        0.611        0.827
   36     33       3    0.658  0.0587        0.553        0.784
   48     25       3    0.595  0.0634        0.483        0.733
   60     19       3    0.515  0.0697        0.395        0.671
  120      5       5    0.354  0.0798        0.228        0.551

                RX_SUMM_RADIATION_F=Radioactive Implants 
        time       n.risk      n.event     survival      std.err lower 95% CI upper 95% CI 
          12            1            0            1            0            1            1 

                RX_SUMM_RADIATION_F=Beam + Imp or Isotopes 
 time n.risk n.event survival std.err lower 95% CI upper 95% CI
   12      1       0        1       0            1            1
   24      1       0        1       0            1            1
   36      1       0        1       0            1            1
   48      1       0        1       0            1            1
   60      1       0        1       0            1            1

                RX_SUMM_RADIATION_F=Unknown 
 time n.risk n.event survival std.err lower 95% CI upper 95% CI
   12     14       1    0.933  0.0644        0.815            1
   24     14       0    0.933  0.0644        0.815            1
   36     13       0    0.933  0.0644        0.815            1
   48     13       0    0.933  0.0644        0.815            1
   60     12       0    0.933  0.0644        0.815            1
  120      5       2    0.770  0.1179        0.570            1




   
## Univariable Cox Proportional Hazard Model for:  RX_SUMM_RADIATION_F
Loglik converged before variable  2,4 ; beta may be infinite. X matrix deemed to be singular; variable 3 5
Call:
coxph(formula = Surv(DX_LASTCONTACT_DEATH_MONTHS, PUF_VITAL_STATUS == 
    0) ~ RX_SUMM_RADIATION_F, data = data)

  n= 1360, number of events= 329 

                                                coef  exp(coef)   se(coef)      z Pr(>|z|)    
RX_SUMM_RADIATION_FBeam Radiation          1.037e+00  2.819e+00  1.794e-01  5.779 7.52e-09 ***
RX_SUMM_RADIATION_FRadioactive Implants   -1.306e+01  2.122e-06  2.871e+03 -0.005    0.996    
RX_SUMM_RADIATION_FRadioisotopes                  NA         NA  0.000e+00     NA       NA    
RX_SUMM_RADIATION_FBeam + Imp or Isotopes -1.305e+01  2.140e-06  1.133e+03 -0.012    0.991    
RX_SUMM_RADIATION_FRadiation, NOS                 NA         NA  0.000e+00     NA       NA    
RX_SUMM_RADIATION_FUnknown                -4.143e-01  6.608e-01  5.046e-01 -0.821    0.412    
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

                                          exp(coef) exp(-coef) lower .95 upper .95
RX_SUMM_RADIATION_FBeam Radiation         2.819e+00  3.547e-01    1.9837     4.007
RX_SUMM_RADIATION_FRadioactive Implants   2.122e-06  4.712e+05    0.0000       Inf
RX_SUMM_RADIATION_FRadioisotopes                 NA         NA        NA        NA
RX_SUMM_RADIATION_FBeam + Imp or Isotopes 2.140e-06  4.673e+05    0.0000       Inf
RX_SUMM_RADIATION_FRadiation, NOS                NA         NA        NA        NA
RX_SUMM_RADIATION_FUnknown                6.608e-01  1.513e+00    0.2458     1.777

Concordance= 0.548  (se = 0.007 )
Rsquare= 0.02   (max possible= 0.956 )
Likelihood ratio test= 27.64  on 4 df,   p=1.478e-05
Wald test            = 34.45  on 4 df,   p=6.024e-07
Score (logrank) test = 38.17  on 4 df,   p=1.034e-07
Transformation introduced infinite values in continuous y-axisTransformation introduced infinite values in continuous y-axisTransformation introduced infinite values in continuous y-axisRemoved 3 rows containing missing values (geom_errorbar).Removed 7 rows containing missing values (geom_text).Removed 7 rows containing missing values (geom_text).Removed 7 rows containing missing values (geom_text).Removed 7 rows containing missing values (geom_text).Removed 7 rows containing missing values (geom_text).Removed 1 rows containing missing values (geom_text).



   
## Unadjusted Kaplan Meier Overall Survival Curve for:  RX_SUMM_RADIATION_F

Lymphovascular Invasion

uni_var(test_var = "LYMPH_VASCULAR_INVASION_F", data_imp = data)
_________________________________________________
   
## LYMPH_VASCULAR_INVASION_F
_________________________________________________
Call: survfit(formula = Surv(DX_LASTCONTACT_DEATH_MONTHS, PUF_VITAL_STATUS == 
    0) ~ LYMPH_VASCULAR_INVASION_F, data = data)

   622 observations deleted due to missingness 
                                              n events median 0.95LCL 0.95UCL
LYMPH_VASCULAR_INVASION_F=Neg_LymphVasc_Inv 271     43     NA    80.1      NA
LYMPH_VASCULAR_INVASION_F=Pos_LumphVasc_Inv  28     15   27.8    20.9      NA
LYMPH_VASCULAR_INVASION_F=N_A                 1      0     NA      NA      NA
LYMPH_VASCULAR_INVASION_F=Unknown           438     61     NA      NA      NA

Call: survfit(formula = Surv(DX_LASTCONTACT_DEATH_MONTHS, PUF_VITAL_STATUS == 
    0) ~ LYMPH_VASCULAR_INVASION_F, data = data)

622 observations deleted due to missingness 
                LYMPH_VASCULAR_INVASION_F=Neg_LymphVasc_Inv 
 time n.risk n.event survival std.err lower 95% CI upper 95% CI
   12    234       8    0.968  0.0111        0.947        0.990
   24    196      10    0.924  0.0172        0.891        0.959
   36    147       7    0.887  0.0215        0.846        0.931
   48    100       4    0.859  0.0252        0.811        0.910
   60     62       8    0.776  0.0361        0.708        0.850

                LYMPH_VASCULAR_INVASION_F=Pos_LumphVasc_Inv 
 time n.risk n.event survival std.err lower 95% CI upper 95% CI
   12     22       5    0.816  0.0744        0.682        0.976
   24     11       7    0.515  0.1030        0.348        0.762
   36      7       1    0.458  0.1062        0.290        0.721
   48      3       2    0.294  0.1180        0.134        0.646
   60      3       0    0.294  0.1180        0.134        0.646

                LYMPH_VASCULAR_INVASION_F=N_A 
     time n.risk n.event survival std.err lower 95% CI upper 95% CI

                LYMPH_VASCULAR_INVASION_F=Unknown 
 time n.risk n.event survival std.err lower 95% CI upper 95% CI
   12    372      10    0.975 0.00772        0.960        0.991
   24    303      15    0.933 0.01301        0.908        0.959
   36    216      14    0.884 0.01775        0.850        0.920
   48    165       3    0.871 0.01913        0.834        0.909
   60    108       9    0.816 0.02524        0.768        0.867




   
## Univariable Cox Proportional Hazard Model for:  LYMPH_VASCULAR_INVASION_F
Loglik converged before variable  2 ; beta may be infinite. 
Call:
coxph(formula = Surv(DX_LASTCONTACT_DEATH_MONTHS, PUF_VITAL_STATUS == 
    0) ~ LYMPH_VASCULAR_INVASION_F, data = data)

  n= 738, number of events= 119 
   (622 observations deleted due to missingness)

                                                 coef  exp(coef)   se(coef)      z Pr(>|z|)    
LYMPH_VASCULAR_INVASION_FPos_LumphVasc_Inv  1.684e+00  5.389e+00  3.019e-01  5.579 2.42e-08 ***
LYMPH_VASCULAR_INVASION_FN_A               -1.057e+01  2.557e-05  1.655e+03 -0.006    0.995    
LYMPH_VASCULAR_INVASION_FUnknown           -1.111e-01  8.948e-01  1.992e-01 -0.558    0.577    
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

                                           exp(coef) exp(-coef) lower .95 upper .95
LYMPH_VASCULAR_INVASION_FPos_LumphVasc_Inv 5.389e+00  1.856e-01    2.9819     9.739
LYMPH_VASCULAR_INVASION_FN_A               2.557e-05  3.911e+04    0.0000       Inf
LYMPH_VASCULAR_INVASION_FUnknown           8.948e-01  1.118e+00    0.6056     1.322

Concordance= 0.575  (se = 0.025 )
Rsquare= 0.035   (max possible= 0.851 )
Likelihood ratio test= 26.52  on 3 df,   p=7.429e-06
Wald test            = 39.82  on 3 df,   p=1.166e-08
Score (logrank) test = 51.01  on 3 df,   p=4.857e-11
Transformation introduced infinite values in continuous y-axisTransformation introduced infinite values in continuous y-axisTransformation introduced infinite values in continuous y-axisRemoved 1 rows containing missing values (geom_errorbar).Removed 4 rows containing missing values (geom_text).Removed 4 rows containing missing values (geom_text).Removed 4 rows containing missing values (geom_text).Removed 4 rows containing missing values (geom_text).Removed 4 rows containing missing values (geom_text).Removed 1 rows containing missing values (geom_text).



   
## Unadjusted Kaplan Meier Overall Survival Curve for:  LYMPH_VASCULAR_INVASION_F

Endoscopic/Robotic

uni_var(test_var = "RX_HOSP_SURG_APPR_2010_F", data_imp = data)
_________________________________________________
   
## RX_HOSP_SURG_APPR_2010_F
_________________________________________________
Call: survfit(formula = Surv(DX_LASTCONTACT_DEATH_MONTHS, PUF_VITAL_STATUS == 
    0) ~ RX_HOSP_SURG_APPR_2010_F, data = data)

   622 observations deleted due to missingness 
                                            n events median 0.95LCL 0.95UCL
RX_HOSP_SURG_APPR_2010_F=No_Surg          122     38   61.9    53.5      NA
RX_HOSP_SURG_APPR_2010_F=Robot_Assist       2      0     NA      NA      NA
RX_HOSP_SURG_APPR_2010_F=Endo_Lap          18      1     NA      NA      NA
RX_HOSP_SURG_APPR_2010_F=Endo_Lap_to_Open   2      0     NA      NA      NA
RX_HOSP_SURG_APPR_2010_F=Open_Unknown     594     80     NA      NA      NA

Call: survfit(formula = Surv(DX_LASTCONTACT_DEATH_MONTHS, PUF_VITAL_STATUS == 
    0) ~ RX_HOSP_SURG_APPR_2010_F, data = data)

622 observations deleted due to missingness 
                RX_HOSP_SURG_APPR_2010_F=No_Surg 
 time n.risk n.event survival std.err lower 95% CI upper 95% CI
   12     94       9    0.920  0.0257        0.871        0.972
   24     62      14    0.766  0.0434        0.686        0.856
   36     39       7    0.667  0.0516        0.574        0.777
   48     24       3    0.607  0.0576        0.504        0.731
   60      9       2    0.527  0.0728        0.402        0.691

                RX_HOSP_SURG_APPR_2010_F=Robot_Assist 
 time n.risk n.event survival std.err lower 95% CI upper 95% CI
   12      2       0        1       0            1            1
   24      2       0        1       0            1            1

                RX_HOSP_SURG_APPR_2010_F=Endo_Lap 
 time n.risk n.event survival std.err lower 95% CI upper 95% CI
   12     15       0      1.0   0.000        1.000            1
   24     15       0      1.0   0.000        1.000            1
   36     12       0      1.0   0.000        1.000            1
   48      7       0      1.0   0.000        1.000            1
   60      4       1      0.8   0.179        0.516            1

                RX_HOSP_SURG_APPR_2010_F=Endo_Lap_to_Open 
 time n.risk n.event survival std.err lower 95% CI upper 95% CI
   12      2       0        1       0            1            1
   24      2       0        1       0            1            1
   36      2       0        1       0            1            1
   48      2       0        1       0            1            1
   60      2       0        1       0            1            1

                RX_HOSP_SURG_APPR_2010_F=Open_Unknown 
 time n.risk n.event survival std.err lower 95% CI upper 95% CI
   12    515      14    0.975 0.00672        0.961        0.988
   24    429      18    0.938 0.01066        0.917        0.959
   36    317      15    0.901 0.01388        0.874        0.929
   48    235       6    0.881 0.01585        0.850        0.913
   60    158      14    0.821 0.02141        0.780        0.864




   
## Univariable Cox Proportional Hazard Model for:  RX_HOSP_SURG_APPR_2010_F
Loglik converged before variable  1,4 ; beta may be infinite. X matrix deemed to be singular; variable 2 6
Call:
coxph(formula = Surv(DX_LASTCONTACT_DEATH_MONTHS, PUF_VITAL_STATUS == 
    0) ~ RX_HOSP_SURG_APPR_2010_F, data = data)

  n= 738, number of events= 119 
   (622 observations deleted due to missingness)

                                               coef  exp(coef)   se(coef)      z Pr(>|z|)    
RX_HOSP_SURG_APPR_2010_FRobot_Assist     -1.648e+01  6.934e-08  4.595e+03 -0.004   0.9971    
RX_HOSP_SURG_APPR_2010_FRobot_to_Open            NA         NA  0.000e+00     NA       NA    
RX_HOSP_SURG_APPR_2010_FEndo_Lap         -2.262e+00  1.041e-01  1.014e+00 -2.231   0.0257 *  
RX_HOSP_SURG_APPR_2010_FEndo_Lap_to_Open -1.647e+01  7.063e-08  2.325e+03 -0.007   0.9943    
RX_HOSP_SURG_APPR_2010_FOpen_Unknown     -1.316e+00  2.682e-01  2.007e-01 -6.557  5.5e-11 ***
RX_HOSP_SURG_APPR_2010_FUnknown                  NA         NA  0.000e+00     NA       NA    
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

                                         exp(coef) exp(-coef) lower .95 upper .95
RX_HOSP_SURG_APPR_2010_FRobot_Assist     6.934e-08  1.442e+07   0.00000       Inf
RX_HOSP_SURG_APPR_2010_FRobot_to_Open           NA         NA        NA        NA
RX_HOSP_SURG_APPR_2010_FEndo_Lap         1.041e-01  9.604e+00   0.01427    0.7596
RX_HOSP_SURG_APPR_2010_FEndo_Lap_to_Open 7.063e-08  1.416e+07   0.00000       Inf
RX_HOSP_SURG_APPR_2010_FOpen_Unknown     2.682e-01  3.728e+00   0.18101    0.3975
RX_HOSP_SURG_APPR_2010_FUnknown                 NA         NA        NA        NA

Concordance= 0.628  (se = 0.019 )
Rsquare= 0.053   (max possible= 0.851 )
Likelihood ratio test= 40.19  on 4 df,   p=3.947e-08
Wald test            = 44.83  on 4 df,   p=4.322e-09
Score (logrank) test = 53.43  on 4 df,   p=6.942e-11
Transformation introduced infinite values in continuous y-axisTransformation introduced infinite values in continuous y-axisTransformation introduced infinite values in continuous y-axisRemoved 3 rows containing missing values (geom_errorbar).Removed 7 rows containing missing values (geom_text).Removed 7 rows containing missing values (geom_text).Removed 7 rows containing missing values (geom_text).Removed 7 rows containing missing values (geom_text).Removed 7 rows containing missing values (geom_text).Removed 1 rows containing missing values (geom_text).



   
## Unadjusted Kaplan Meier Overall Survival Curve for:  RX_HOSP_SURG_APPR_2010_F

Surgery Radiation Sequence

uni_var(test_var = "SURG_RAD_SEQ", data_imp = data)
_________________________________________________
   
## SURG_RAD_SEQ
_________________________________________________
Call: survfit(formula = Surv(DX_LASTCONTACT_DEATH_MONTHS, PUF_VITAL_STATUS == 
    0) ~ SURG_RAD_SEQ, data = data)

                              n events median 0.95LCL 0.95UCL
SURG_RAD_SEQ=Surg Alone    1158    241  155.4   141.8      NA
SURG_RAD_SEQ=Surg then Rad   23      9  125.6    39.2      NA
SURG_RAD_SEQ=Rad Alone       52     26   55.2    40.0      NA
SURG_RAD_SEQ=No Treatment   105     47   68.5    48.6    81.8
SURG_RAD_SEQ=Other           22      6  131.9    82.2      NA

Call: survfit(formula = Surv(DX_LASTCONTACT_DEATH_MONTHS, PUF_VITAL_STATUS == 
    0) ~ SURG_RAD_SEQ, data = data)

                SURG_RAD_SEQ=Surg Alone 
 time n.risk n.event survival std.err lower 95% CI upper 95% CI
   12   1018      28    0.974 0.00477        0.965        0.984
   24    911      27    0.948 0.00689        0.934        0.961
   36    771      31    0.913 0.00902        0.896        0.931
   48    649      28    0.878 0.01087        0.857        0.899
   60    538      38    0.823 0.01333        0.797        0.850
  120    112      77    0.624 0.02388        0.579        0.673

                SURG_RAD_SEQ=Surg then Rad 
 time n.risk n.event survival std.err lower 95% CI upper 95% CI
   12     23       0    1.000   0.000        1.000        1.000
   24     14       6    0.710   0.100        0.538        0.936
   36     11       0    0.710   0.100        0.538        0.936
   48     10       1    0.645   0.110        0.462        0.901
   60     10       0    0.645   0.110        0.462        0.901
  120      4       1    0.581   0.116        0.392        0.860

                SURG_RAD_SEQ=Rad Alone 
 time n.risk n.event survival std.err lower 95% CI upper 95% CI
   12     43       8    0.846  0.0500       0.7536        0.950
   24     31       6    0.722  0.0635       0.6077        0.858
   36     23       3    0.646  0.0703       0.5221        0.800
   48     16       2    0.585  0.0759       0.4533        0.754
   60     10       3    0.452  0.0894       0.3072        0.666
  120      1       4    0.211  0.1053       0.0795        0.561

                SURG_RAD_SEQ=No Treatment 
 time n.risk n.event survival std.err lower 95% CI upper 95% CI
   12     82      11    0.889  0.0316        0.829        0.953
   24     58      13    0.734  0.0471        0.647        0.833
   36     42       6    0.648  0.0532        0.552        0.761
   48     34       2    0.613  0.0557        0.513        0.733
   60     20       3    0.546  0.0621        0.436        0.682
  120      3      10    0.214  0.0755        0.107        0.427

                SURG_RAD_SEQ=Other 
 time n.risk n.event survival std.err lower 95% CI upper 95% CI
   12     18       1    0.947  0.0512        0.852        1.000
   24     18       0    0.947  0.0512        0.852        1.000
   36     17       0    0.947  0.0512        0.852        1.000
   48     15       1    0.888  0.0748        0.753        1.000
   60     14       0    0.888  0.0748        0.753        1.000
  120      5       3    0.680  0.1203        0.481        0.962




   
## Univariable Cox Proportional Hazard Model for:  SURG_RAD_SEQ
X matrix deemed to be singular; variable 5 6
Call:
coxph(formula = Surv(DX_LASTCONTACT_DEATH_MONTHS, PUF_VITAL_STATUS == 
    0) ~ SURG_RAD_SEQ, data = data)

  n= 1360, number of events= 329 

                                          coef exp(coef) se(coef)      z Pr(>|z|)    
SURG_RAD_SEQSurg then Rad              0.62470   1.86768  0.33984  1.838    0.066 .  
SURG_RAD_SEQRad Alone                  1.41191   4.10379  0.20785  6.793  1.1e-11 ***
SURG_RAD_SEQNo Treatment               1.33626   3.80477  0.16093  8.303  < 2e-16 ***
SURG_RAD_SEQOther                     -0.02054   0.97967  0.41436 -0.050    0.960    
SURG_RAD_SEQRad before and after Surg       NA        NA  0.00000     NA       NA    
SURG_RAD_SEQRad then Surg                   NA        NA  0.00000     NA       NA    
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

                                      exp(coef) exp(-coef) lower .95 upper .95
SURG_RAD_SEQSurg then Rad                1.8677     0.5354    0.9595     3.636
SURG_RAD_SEQRad Alone                    4.1038     0.2437    2.7307     6.167
SURG_RAD_SEQNo Treatment                 3.8048     0.2628    2.7755     5.216
SURG_RAD_SEQOther                        0.9797     1.0207    0.4349     2.207
SURG_RAD_SEQRad before and after Surg        NA         NA        NA        NA
SURG_RAD_SEQRad then Surg                    NA         NA        NA        NA

Concordance= 0.602  (se = 0.01 )
Rsquare= 0.057   (max possible= 0.956 )
Likelihood ratio test= 80.25  on 4 df,   p=1.11e-16
Wald test            = 102.8  on 4 df,   p=0
Score (logrank) test = 118.9  on 4 df,   p=0
Removed 3 rows containing missing values (geom_errorbar).



   
## Unadjusted Kaplan Meier Overall Survival Curve for:  SURG_RAD_SEQ

Surgery Yes/No

model_one <- coxph(Surv(DX_LASTCONTACT_DEATH_MONTHS, PUF_VITAL_STATUS == 0)
                     ~ SURG_RAD_SEQ + INSURANCE_F + AGE + SEX_F + RACE_F + INCOME_F + U_R_F +
                      FACILITY_TYPE_F + FACILITY_LOCATION_F + EDUCATION_F,
                     data = data)
Loglik converged before variable  10 ; beta may be infinite. X matrix deemed to be singular; variable 5 6
model_one %>% summary()
Call:
coxph(formula = Surv(DX_LASTCONTACT_DEATH_MONTHS, PUF_VITAL_STATUS == 
    0) ~ SURG_RAD_SEQ + INSURANCE_F + AGE + SEX_F + RACE_F + 
    INCOME_F + U_R_F + FACILITY_TYPE_F + FACILITY_LOCATION_F + 
    EDUCATION_F, data = data)

  n= 1306, number of events= 320 
   (54 observations deleted due to missingness)

                                                   coef  exp(coef)   se(coef)      z Pr(>|z|)    
SURG_RAD_SEQSurg then Rad                     7.095e-01  2.033e+00  3.659e-01  1.939  0.05252 .  
SURG_RAD_SEQRad Alone                         7.361e-01  2.088e+00  2.342e-01  3.143  0.00167 ** 
SURG_RAD_SEQNo Treatment                      8.477e-01  2.334e+00  1.788e-01  4.741 2.13e-06 ***
SURG_RAD_SEQOther                            -3.561e-01  7.004e-01  4.744e-01 -0.750  0.45297    
SURG_RAD_SEQRad before and after Surg                NA         NA  0.000e+00     NA       NA    
SURG_RAD_SEQRad then Surg                            NA         NA  0.000e+00     NA       NA    
INSURANCE_FNone                               8.065e-01  2.240e+00  5.304e-01  1.521  0.12833    
INSURANCE_FMedicaid                          -2.998e-01  7.409e-01  6.605e-01 -0.454  0.64986    
INSURANCE_FMedicare                           1.934e-01  1.213e+00  1.713e-01  1.129  0.25909    
INSURANCE_FOther Government                  -1.292e+01  2.445e-06  9.794e+02 -0.013  0.98947    
INSURANCE_FUnknown                           -1.468e-01  8.635e-01  6.105e-01 -0.240  0.81000    
AGE                                           8.308e-02  1.087e+00  7.292e-03 11.393  < 2e-16 ***
SEX_FFemale                                  -4.088e-01  6.644e-01  1.412e-01 -2.895  0.00380 ** 
RACE_FBlack                                   9.557e-01  2.600e+00  3.960e-01  2.413  0.01581 *  
RACE_FOther/Unk                               3.420e-01  1.408e+00  3.254e-01  1.051  0.29327    
RACE_FAsian                                  -5.815e-01  5.591e-01  4.019e-01 -1.447  0.14796    
INCOME_F$38,000 - $47,999                    -1.102e-01  8.956e-01  2.130e-01 -0.517  0.60485    
INCOME_F$48,000 - $62,999                     1.069e-02  1.011e+00  2.236e-01  0.048  0.96187    
INCOME_F$63,000 +                             4.084e-02  1.042e+00  2.508e-01  0.163  0.87065    
U_R_FUrban                                    1.305e-01  1.139e+00  1.802e-01  0.724  0.46895    
U_R_FRural                                   -7.241e-01  4.847e-01  4.462e-01 -1.623  0.10459    
FACILITY_TYPE_FComprehensive Comm Ca Program  1.846e-01  1.203e+00  3.885e-01  0.475  0.63475    
FACILITY_TYPE_FAcademic/Research Program     -1.418e-03  9.986e-01  3.872e-01 -0.004  0.99708    
FACILITY_TYPE_FIntegrated Network Ca Program  3.764e-01  1.457e+00  4.014e-01  0.938  0.34846    
FACILITY_LOCATION_FMiddle Atlantic           -3.683e-01  6.919e-01  3.001e-01 -1.227  0.21976    
FACILITY_LOCATION_FSouth Atlantic            -5.749e-01  5.627e-01  3.100e-01 -1.855  0.06365 .  
FACILITY_LOCATION_FEast North Central        -2.367e-01  7.892e-01  2.966e-01 -0.798  0.42480    
FACILITY_LOCATION_FEast South Central        -4.062e-01  6.662e-01  3.659e-01 -1.110  0.26698    
FACILITY_LOCATION_FWest North Central         1.014e-02  1.010e+00  3.132e-01  0.032  0.97417    
FACILITY_LOCATION_FWest South Central        -2.580e-01  7.726e-01  3.393e-01 -0.760  0.44703    
FACILITY_LOCATION_FMountain                  -1.218e-02  9.879e-01  3.366e-01 -0.036  0.97113    
FACILITY_LOCATION_FPacific                   -2.346e-01  7.909e-01  3.207e-01 -0.731  0.46448    
EDUCATION_F13 - 20.9%                        -2.889e-01  7.491e-01  2.182e-01 -1.324  0.18545    
EDUCATION_F7 - 12.9%                         -2.523e-01  7.770e-01  2.239e-01 -1.127  0.25986    
EDUCATION_FLess than 7%                      -5.739e-01  5.633e-01  2.605e-01 -2.203  0.02761 *  
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

                                             exp(coef) exp(-coef) lower .95 upper .95
SURG_RAD_SEQSurg then Rad                    2.033e+00  4.919e-01    0.9923    4.1651
SURG_RAD_SEQRad Alone                        2.088e+00  4.790e-01    1.3193    3.3038
SURG_RAD_SEQNo Treatment                     2.334e+00  4.284e-01    1.6442    3.3140
SURG_RAD_SEQOther                            7.004e-01  1.428e+00    0.2764    1.7750
SURG_RAD_SEQRad before and after Surg               NA         NA        NA        NA
SURG_RAD_SEQRad then Surg                           NA         NA        NA        NA
INSURANCE_FNone                              2.240e+00  4.464e-01    0.7922    6.3345
INSURANCE_FMedicaid                          7.409e-01  1.350e+00    0.2030    2.7039
INSURANCE_FMedicare                          1.213e+00  8.242e-01    0.8672    1.6975
INSURANCE_FOther Government                  2.445e-06  4.090e+05    0.0000       Inf
INSURANCE_FUnknown                           8.635e-01  1.158e+00    0.2610    2.8571
AGE                                          1.087e+00  9.203e-01    1.0712    1.1023
SEX_FFemale                                  6.644e-01  1.505e+00    0.5038    0.8763
RACE_FBlack                                  2.600e+00  3.846e-01    1.1966    5.6510
RACE_FOther/Unk                              1.408e+00  7.104e-01    0.7440    2.6637
RACE_FAsian                                  5.591e-01  1.789e+00    0.2543    1.2291
INCOME_F$38,000 - $47,999                    8.956e-01  1.117e+00    0.5900    1.3597
INCOME_F$48,000 - $62,999                    1.011e+00  9.894e-01    0.6520    1.5668
INCOME_F$63,000 +                            1.042e+00  9.600e-01    0.6372    1.7030
U_R_FUrban                                   1.139e+00  8.777e-01    0.8004    1.6219
U_R_FRural                                   4.847e-01  2.063e+00    0.2022    1.1622
FACILITY_TYPE_FComprehensive Comm Ca Program 1.203e+00  8.315e-01    0.5616    2.5757
FACILITY_TYPE_FAcademic/Research Program     9.986e-01  1.001e+00    0.4676    2.1327
FACILITY_TYPE_FIntegrated Network Ca Program 1.457e+00  6.864e-01    0.6634    3.1999
FACILITY_LOCATION_FMiddle Atlantic           6.919e-01  1.445e+00    0.3842    1.2460
FACILITY_LOCATION_FSouth Atlantic            5.627e-01  1.777e+00    0.3065    1.0332
FACILITY_LOCATION_FEast North Central        7.892e-01  1.267e+00    0.4413    1.4114
FACILITY_LOCATION_FEast South Central        6.662e-01  1.501e+00    0.3252    1.3648
FACILITY_LOCATION_FWest North Central        1.010e+00  9.899e-01    0.5468    1.8664
FACILITY_LOCATION_FWest South Central        7.726e-01  1.294e+00    0.3973    1.5024
FACILITY_LOCATION_FMountain                  9.879e-01  1.012e+00    0.5108    1.9107
FACILITY_LOCATION_FPacific                   7.909e-01  1.264e+00    0.4218    1.4829
EDUCATION_F13 - 20.9%                        7.491e-01  1.335e+00    0.4885    1.1488
EDUCATION_F7 - 12.9%                         7.770e-01  1.287e+00    0.5010    1.2051
EDUCATION_FLess than 7%                      5.633e-01  1.775e+00    0.3381    0.9387

Concordance= 0.769  (se = 0.018 )
Rsquare= 0.218   (max possible= 0.956 )
Likelihood ratio test= 321.2  on 33 df,   p=0
Wald test            = 282.4  on 33 df,   p=0
Score (logrank) test = 323.3  on 33 df,   p=0

Radiation Yes/No

uni_var(test_var = "RADIATION_YN", data_imp = data)
_________________________________________________
   
## RADIATION_YN
_________________________________________________
Call: survfit(formula = Surv(DX_LASTCONTACT_DEATH_MONTHS, PUF_VITAL_STATUS == 
    0) ~ RADIATION_YN, data = data)

   17 observations deleted due to missingness 
                    n events median 0.95LCL 0.95UCL
RADIATION_YN=No  1268    290  149.6   128.5      NA
RADIATION_YN=Yes   75     35   61.9    40.6      NA

Call: survfit(formula = Surv(DX_LASTCONTACT_DEATH_MONTHS, PUF_VITAL_STATUS == 
    0) ~ RADIATION_YN, data = data)

17 observations deleted due to missingness 
                RADIATION_YN=No 
 time n.risk n.event survival std.err lower 95% CI upper 95% CI
   12   1104      39    0.968 0.00512        0.958        0.978
   24    973      40    0.931 0.00752        0.916        0.946
   36    817      37    0.893 0.00947        0.874        0.912
   48    685      31    0.857 0.01110        0.835        0.879
   60    560      41    0.802 0.01330        0.776        0.828
  120    115      88    0.595 0.02306        0.551        0.642

                RADIATION_YN=Yes 
 time n.risk n.event survival std.err lower 95% CI upper 95% CI
   12     66       8    0.893  0.0356        0.826        0.966
   24     45      12    0.718  0.0538        0.620        0.832
   36     34       3    0.666  0.0578        0.561        0.789
   48     26       3    0.604  0.0625        0.493        0.740
   60     20       3    0.526  0.0687        0.407        0.680
  120      5       5    0.367  0.0803        0.239        0.563




   
## Univariable Cox Proportional Hazard Model for:  RADIATION_YN

Call:
coxph(formula = Surv(DX_LASTCONTACT_DEATH_MONTHS, PUF_VITAL_STATUS == 
    0) ~ RADIATION_YN, data = data)

  n= 1343, number of events= 325 
   (17 observations deleted due to missingness)

                  coef exp(coef) se(coef)     z Pr(>|z|)    
RADIATION_YNYes 1.0029    2.7262   0.1793 5.592 2.25e-08 ***
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

                exp(coef) exp(-coef) lower .95 upper .95
RADIATION_YNYes     2.726     0.3668     1.918     3.874

Concordance= 0.544  (se = 0.007 )
Rsquare= 0.018   (max possible= 0.955 )
Likelihood ratio test= 24.23  on 1 df,   p=8.545e-07
Wald test            = 31.27  on 1 df,   p=2.246e-08
Score (logrank) test = 33.97  on 1 df,   p=5.587e-09





   
## Unadjusted Kaplan Meier Overall Survival Curve for:  RADIATION_YN

Chemo Yes/No

uni_var(test_var = "CHEMO_YN", data_imp = data)
_________________________________________________
   
## CHEMO_YN
_________________________________________________
Call: survfit(formula = Surv(DX_LASTCONTACT_DEATH_MONTHS, PUF_VITAL_STATUS == 
    0) ~ CHEMO_YN, data = data)

                n events median 0.95LCL 0.95UCL
CHEMO_YN=No  1287    298  149.6   128.2      NA
CHEMO_YN=Yes   31     20   30.3    21.9      NA
CHEMO_YN=Ukn   42     11     NA   101.5      NA

Call: survfit(formula = Surv(DX_LASTCONTACT_DEATH_MONTHS, PUF_VITAL_STATUS == 
    0) ~ CHEMO_YN, data = data)

                CHEMO_YN=No 
 time n.risk n.event survival std.err lower 95% CI upper 95% CI
   12   1119      44    0.964 0.00534        0.954        0.974
   24    981      42    0.926 0.00770        0.911        0.941
   36    823      34    0.891 0.00944        0.873        0.910
   48    691      30    0.857 0.01100        0.835        0.878
   60    566      43    0.800 0.01327        0.774        0.826
  120    117      89    0.596 0.02278        0.553        0.642

                CHEMO_YN=Yes 
 time n.risk n.event survival std.err lower 95% CI upper 95% CI
   12     26       3    0.898  0.0560       0.7942        1.000
   24     15       9    0.581  0.0927       0.4247        0.794
   36     10       3    0.456  0.0968       0.3011        0.692
   48      5       3    0.299  0.0981       0.1576        0.569
   60      2       1    0.200  0.1045       0.0716        0.557

                CHEMO_YN=Ukn 
 time n.risk n.event survival std.err lower 95% CI upper 95% CI
   12     39       1    0.975  0.0247        0.928        1.000
   24     36       1    0.950  0.0345        0.885        1.000
   36     31       3    0.868  0.0553        0.766        0.983
   48     28       1    0.839  0.0605        0.728        0.966
   60     24       0    0.839  0.0605        0.728        0.966
  120      8       5    0.618  0.0975        0.453        0.842




   
## Univariable Cox Proportional Hazard Model for:  CHEMO_YN

Call:
coxph(formula = Surv(DX_LASTCONTACT_DEATH_MONTHS, PUF_VITAL_STATUS == 
    0) ~ CHEMO_YN, data = data)

  n= 1360, number of events= 329 

               coef exp(coef) se(coef)      z Pr(>|z|)    
CHEMO_YNYes  1.8818    6.5650   0.2352  8.001 1.22e-15 ***
CHEMO_YNUkn -0.1860    0.8302   0.3079 -0.604    0.546    
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

            exp(coef) exp(-coef) lower .95 upper .95
CHEMO_YNYes    6.5650     0.1523    4.1403    10.410
CHEMO_YNUkn    0.8302     1.2045    0.4541     1.518

Concordance= 0.535  (se = 0.007 )
Rsquare= 0.029   (max possible= 0.956 )
Likelihood ratio test= 40.5  on 2 df,   p=1.605e-09
Wald test            = 64.85  on 2 df,   p=8.216e-15
Score (logrank) test = 86.36  on 2 df,   p=0
Removed 1 rows containing missing values (geom_errorbar).



   
## Unadjusted Kaplan Meier Overall Survival Curve for:  CHEMO_YN

Treatment Yes/No

uni_var(test_var = "Tx_YN", data_imp = data)
_________________________________________________
   
## Tx_YN
_________________________________________________
Call: survfit(formula = Surv(DX_LASTCONTACT_DEATH_MONTHS, PUF_VITAL_STATUS == 
    0) ~ Tx_YN, data = data)

   42 observations deleted due to missingness 
               n events median 0.95LCL 0.95UCL
Tx_YN=FALSE   56     30   63.2    29.5    81.8
Tx_YN=TRUE  1262    288  149.6   130.5      NA

Call: survfit(formula = Surv(DX_LASTCONTACT_DEATH_MONTHS, PUF_VITAL_STATUS == 
    0) ~ Tx_YN, data = data)

42 observations deleted due to missingness 
                Tx_YN=FALSE 
 time n.risk n.event survival std.err lower 95% CI upper 95% CI
   12     43       9    0.834  0.0507       0.7400        0.939
   24     31       7    0.687  0.0655       0.5700        0.828
   36     20       4    0.587  0.0729       0.4605        0.749
   48     17       1    0.556  0.0753       0.4268        0.725
   60     12       1    0.517  0.0797       0.3818        0.699
  120      2       7    0.194  0.0846       0.0824        0.456

                Tx_YN=TRUE 
 time n.risk n.event survival std.err lower 95% CI upper 95% CI
   12   1102      38    0.968 0.00509        0.958        0.978
   24    965      44    0.928 0.00771        0.913        0.943
   36    813      33    0.893 0.00945        0.875        0.912
   48    679      32    0.856 0.01115        0.834        0.878
   60    556      43    0.798 0.01345        0.772        0.825
  120    115      83    0.602 0.02298        0.558        0.648




   
## Univariable Cox Proportional Hazard Model for:  Tx_YN

Call:
coxph(formula = Surv(DX_LASTCONTACT_DEATH_MONTHS, PUF_VITAL_STATUS == 
    0) ~ Tx_YN, data = data)

  n= 1318, number of events= 318 
   (42 observations deleted due to missingness)

             coef exp(coef) se(coef)      z Pr(>|z|)    
Tx_YNTRUE -1.3343    0.2633   0.1926 -6.927 4.29e-12 ***
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

          exp(coef) exp(-coef) lower .95 upper .95
Tx_YNTRUE    0.2633      3.798    0.1805    0.3841

Concordance= 0.542  (se = 0.006 )
Rsquare= 0.026   (max possible= 0.954 )
Likelihood ratio test= 34.11  on 1 df,   p=5.202e-09
Wald test            = 47.99  on 1 df,   p=4.292e-12
Score (logrank) test = 55.52  on 1 df,   p=9.248e-14





   
## Unadjusted Kaplan Meier Overall Survival Curve for:  Tx_YN

Metastases at Dx

uni_var(test_var = "mets_at_dx_F", data_imp = data)
_________________________________________________
   
## mets_at_dx_F
_________________________________________________
Call: survfit(formula = Surv(DX_LASTCONTACT_DEATH_MONTHS, PUF_VITAL_STATUS == 
    0) ~ mets_at_dx_F, data = data)

                      n events median 0.95LCL 0.95UCL
mets_at_dx_F=FALSE 1355    324  149.6  128.20      NA
mets_at_dx_F=TRUE     5      5   10.8    8.67      NA

Call: survfit(formula = Surv(DX_LASTCONTACT_DEATH_MONTHS, PUF_VITAL_STATUS == 
    0) ~ mets_at_dx_F, data = data)

                mets_at_dx_F=FALSE 
 time n.risk n.event survival std.err lower 95% CI upper 95% CI
   12   1182      45    0.965 0.00513        0.955        0.975
   24   1031      51    0.921 0.00772        0.906        0.937
   36    864      39    0.884 0.00947        0.865        0.903
   48    724      34    0.847 0.01100        0.825        0.869
   60    592      44    0.792 0.01306        0.767        0.818
  120    125      95    0.589 0.02194        0.547        0.633

                mets_at_dx_F=TRUE 
 time n.risk n.event survival std.err lower 95% CI upper 95% CI
   12      2       3      0.4   0.219       0.1367            1
   24      1       1      0.2   0.179       0.0346            1




   
## Univariable Cox Proportional Hazard Model for:  mets_at_dx_F

Call:
coxph(formula = Surv(DX_LASTCONTACT_DEATH_MONTHS, PUF_VITAL_STATUS == 
    0) ~ mets_at_dx_F, data = data)

  n= 1360, number of events= 329 

                    coef exp(coef) se(coef)    z Pr(>|z|)    
mets_at_dx_FTRUE  3.2623   26.1097   0.4614 7.07 1.55e-12 ***
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

                 exp(coef) exp(-coef) lower .95 upper .95
mets_at_dx_FTRUE     26.11     0.0383     10.57      64.5

Concordance= 0.511  (se = 0.001 )
Rsquare= 0.017   (max possible= 0.956 )
Likelihood ratio test= 22.71  on 1 df,   p=1.882e-06
Wald test            = 49.99  on 1 df,   p=1.546e-12
Score (logrank) test = 113.3  on 1 df,   p=0





   
## Unadjusted Kaplan Meier Overall Survival Curve for:  mets_at_dx_F

Tumor specific Variables

Node Size

Cox Proportional Hazard Ratio

Model #1

Full analysis

model_one <- coxph(Surv(DX_LASTCONTACT_DEATH_MONTHS, PUF_VITAL_STATUS == 0)
                     ~ FACILITY_TYPE_F + FACILITY_LOCATION_F + CROWFLY + 
                 DX_STAGING_PROC_DAYS + 
                 CHEMO_YN + RADIATION_YN + SURGERY_YN + IMMUNO_YN +
                 AGE_F + SEX_F + RACE_F + HISPANIC + INSURANCE_F + INCOME_F + 
                 EDUCATION_F + YEAR_OF_DIAGNOSIS,
                     data = data)
Loglik converged before variable  33 ; beta may be infinite. X matrix deemed to be singular; variable 52
model_one %>% summary()
Call:
coxph(formula = Surv(DX_LASTCONTACT_DEATH_MONTHS, PUF_VITAL_STATUS == 
    0) ~ FACILITY_TYPE_F + FACILITY_LOCATION_F + CROWFLY + DX_STAGING_PROC_DAYS + 
    CHEMO_YN + RADIATION_YN + SURGERY_YN + IMMUNO_YN + AGE_F + 
    SEX_F + RACE_F + HISPANIC + INSURANCE_F + INCOME_F + EDUCATION_F + 
    YEAR_OF_DIAGNOSIS, data = data)

  n= 918, number of events= 239 
   (442 observations deleted due to missingness)

                                                   coef  exp(coef)   se(coef)      z Pr(>|z|)    
FACILITY_TYPE_FComprehensive Comm Ca Program  1.166e+00  3.208e+00  6.244e-01  1.867 0.061939 .  
FACILITY_TYPE_FAcademic/Research Program      1.042e+00  2.836e+00  6.263e-01  1.665 0.096003 .  
FACILITY_TYPE_FIntegrated Network Ca Program  1.493e+00  4.450e+00  6.399e-01  2.333 0.019642 *  
FACILITY_LOCATION_FMiddle Atlantic           -1.138e-01  8.924e-01  3.623e-01 -0.314 0.753423    
FACILITY_LOCATION_FSouth Atlantic            -4.717e-01  6.239e-01  3.756e-01 -1.256 0.209177    
FACILITY_LOCATION_FEast North Central        -1.522e-01  8.588e-01  3.620e-01 -0.420 0.674164    
FACILITY_LOCATION_FEast South Central        -4.270e-02  9.582e-01  4.199e-01 -0.102 0.919002    
FACILITY_LOCATION_FWest North Central         3.202e-01  1.377e+00  3.839e-01  0.834 0.404204    
FACILITY_LOCATION_FWest South Central         1.233e-01  1.131e+00  4.235e-01  0.291 0.770985    
FACILITY_LOCATION_FMountain                   4.214e-01  1.524e+00  4.091e-01  1.030 0.302999    
FACILITY_LOCATION_FPacific                    7.751e-02  1.081e+00  3.852e-01  0.201 0.840540    
CROWFLY                                      -2.308e-03  9.977e-01  1.589e-03 -1.452 0.146440    
DX_STAGING_PROC_DAYS                          2.420e-03  1.002e+00  3.698e-03  0.654 0.512799    
CHEMO_YNYes                                   1.566e+00  4.786e+00  3.107e-01  5.038 4.70e-07 ***
CHEMO_YNUkn                                   2.778e-01  1.320e+00  4.406e-01  0.630 0.528372    
RADIATION_YNYes                               2.805e-01  1.324e+00  2.721e-01  1.031 0.302612    
SURGERY_YNUkn                                 6.195e-01  1.858e+00  7.580e-01  0.817 0.413741    
SURGERY_YNYes                                -7.463e-01  4.741e-01  2.211e-01 -3.375 0.000737 ***
IMMUNO_YNYes                                 -2.505e-01  7.784e-01  3.094e-01 -0.809 0.418233    
IMMUNO_YNUkn                                 -3.512e-01  7.038e-01  1.424e+00 -0.247 0.805138    
AGE_F(54,64]                                  1.721e-01  1.188e+00  4.644e-01  0.371 0.710916    
AGE_F(64,74]                                  8.329e-01  2.300e+00  4.398e-01  1.894 0.058247 .  
AGE_F(74,100]                                 2.003e+00  7.410e+00  4.285e-01  4.674 2.95e-06 ***
SEX_FFemale                                  -1.778e-01  8.371e-01  1.778e-01 -1.000 0.317406    
RACE_FBlack                                   9.986e-01  2.714e+00  4.658e-01  2.144 0.032044 *  
RACE_FOther/Unk                               5.287e-01  1.697e+00  4.259e-01  1.241 0.214492    
RACE_FAsian                                  -5.494e-01  5.773e-01  4.646e-01 -1.182 0.237017    
HISPANICYes                                  -6.543e-01  5.198e-01  4.205e-01 -1.556 0.119723    
HISPANICUnknown                              -2.546e-01  7.752e-01  2.971e-01 -0.857 0.391523    
INSURANCE_FNone                               1.821e-01  1.200e+00  8.644e-01  0.211 0.833158    
INSURANCE_FMedicaid                           3.859e-02  1.039e+00  8.025e-01  0.048 0.961644    
INSURANCE_FMedicare                           2.388e-01  1.270e+00  2.223e-01  1.074 0.282675    
INSURANCE_FOther Government                  -1.368e+01  1.147e-06  1.604e+03 -0.009 0.993194    
INSURANCE_FUnknown                            6.386e-01  1.894e+00  6.522e-01  0.979 0.327515    
INCOME_F$38,000 - $47,999                     1.861e-01  1.205e+00  2.574e-01  0.723 0.469712    
INCOME_F$48,000 - $62,999                     2.305e-01  1.259e+00  2.717e-01  0.848 0.396242    
INCOME_F$63,000 +                             3.424e-01  1.408e+00  2.887e-01  1.186 0.235513    
EDUCATION_F13 - 20.9%                        -5.668e-01  5.673e-01  2.603e-01 -2.178 0.029428 *  
EDUCATION_F7 - 12.9%                         -3.691e-01  6.914e-01  2.595e-01 -1.422 0.154952    
EDUCATION_FLess than 7%                      -9.269e-01  3.958e-01  3.003e-01 -3.086 0.002027 ** 
YEAR_OF_DIAGNOSIS2005                         1.155e-01  1.122e+00  3.306e-01  0.349 0.726783    
YEAR_OF_DIAGNOSIS2006                         1.441e-01  1.155e+00  3.455e-01  0.417 0.676745    
YEAR_OF_DIAGNOSIS2007                         4.388e-01  1.551e+00  3.335e-01  1.316 0.188191    
YEAR_OF_DIAGNOSIS2008                        -9.515e-02  9.092e-01  3.470e-01 -0.274 0.783948    
YEAR_OF_DIAGNOSIS2009                         1.700e-01  1.185e+00  3.594e-01  0.473 0.636134    
YEAR_OF_DIAGNOSIS2010                         3.912e-01  1.479e+00  3.702e-01  1.057 0.290624    
YEAR_OF_DIAGNOSIS2011                         3.524e-01  1.422e+00  3.633e-01  0.970 0.332070    
YEAR_OF_DIAGNOSIS2012                         1.750e-01  1.191e+00  4.214e-01  0.415 0.677954    
YEAR_OF_DIAGNOSIS2013                         6.302e-01  1.878e+00  3.721e-01  1.693 0.090361 .  
YEAR_OF_DIAGNOSIS2014                         1.875e-01  1.206e+00  4.412e-01  0.425 0.670791    
YEAR_OF_DIAGNOSIS2015                         4.412e-01  1.555e+00  4.302e-01  1.026 0.304993    
YEAR_OF_DIAGNOSIS2016                                NA         NA  0.000e+00     NA       NA    
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

                                             exp(coef) exp(-coef) lower .95 upper .95
FACILITY_TYPE_FComprehensive Comm Ca Program 3.208e+00  3.117e-01   0.94345   10.9077
FACILITY_TYPE_FAcademic/Research Program     2.836e+00  3.526e-01   0.83110    9.6781
FACILITY_TYPE_FIntegrated Network Ca Program 4.450e+00  2.247e-01   1.26971   15.5986
FACILITY_LOCATION_FMiddle Atlantic           8.924e-01  1.121e+00   0.43868    1.8155
FACILITY_LOCATION_FSouth Atlantic            6.239e-01  1.603e+00   0.29880    1.3028
FACILITY_LOCATION_FEast North Central        8.588e-01  1.164e+00   0.42245    1.7459
FACILITY_LOCATION_FEast South Central        9.582e-01  1.044e+00   0.42072    2.1823
FACILITY_LOCATION_FWest North Central        1.377e+00  7.260e-01   0.64906    2.9234
FACILITY_LOCATION_FWest South Central        1.131e+00  8.840e-01   0.49320    2.5945
FACILITY_LOCATION_FMountain                  1.524e+00  6.561e-01   0.68353    3.3985
FACILITY_LOCATION_FPacific                   1.081e+00  9.254e-01   0.50786    2.2992
CROWFLY                                      9.977e-01  1.002e+00   0.99459    1.0008
DX_STAGING_PROC_DAYS                         1.002e+00  9.976e-01   0.99518    1.0097
CHEMO_YNYes                                  4.786e+00  2.090e-01   2.60278    8.7992
CHEMO_YNUkn                                  1.320e+00  7.574e-01   0.55667    3.1312
RADIATION_YNYes                              1.324e+00  7.554e-01   0.77660    2.2566
SURGERY_YNUkn                                1.858e+00  5.382e-01   0.42057    8.2090
SURGERY_YNYes                                4.741e-01  2.109e+00   0.30737    0.7313
IMMUNO_YNYes                                 7.784e-01  1.285e+00   0.42448    1.4276
IMMUNO_YNUkn                                 7.038e-01  1.421e+00   0.04321   11.4637
AGE_F(54,64]                                 1.188e+00  8.419e-01   0.47801    2.9517
AGE_F(64,74]                                 2.300e+00  4.348e-01   0.97133    5.4458
AGE_F(74,100]                                7.410e+00  1.349e-01   3.19982   17.1615
SEX_FFemale                                  8.371e-01  1.195e+00   0.59084    1.1861
RACE_FBlack                                  2.714e+00  3.684e-01   1.08943    6.7633
RACE_FOther/Unk                              1.697e+00  5.894e-01   0.73633    3.9096
RACE_FAsian                                  5.773e-01  1.732e+00   0.23226    1.4351
HISPANICYes                                  5.198e-01  1.924e+00   0.22797    1.1852
HISPANICUnknown                              7.752e-01  1.290e+00   0.43302    1.3879
INSURANCE_FNone                              1.200e+00  8.335e-01   0.22043    6.5297
INSURANCE_FMedicaid                          1.039e+00  9.621e-01   0.21562    5.0100
INSURANCE_FMedicare                          1.270e+00  7.876e-01   0.82131    1.9630
INSURANCE_FOther Government                  1.147e-06  8.717e+05   0.00000       Inf
INSURANCE_FUnknown                           1.894e+00  5.280e-01   0.52746    6.7994
INCOME_F$38,000 - $47,999                    1.205e+00  8.302e-01   0.72731    1.9949
INCOME_F$48,000 - $62,999                    1.259e+00  7.941e-01   0.73931    2.1448
INCOME_F$63,000 +                            1.408e+00  7.100e-01   0.79985    2.4799
EDUCATION_F13 - 20.9%                        5.673e-01  1.763e+00   0.34064    0.9449
EDUCATION_F7 - 12.9%                         6.914e-01  1.446e+00   0.41573    1.1497
EDUCATION_FLess than 7%                      3.958e-01  2.527e+00   0.21969    0.7130
YEAR_OF_DIAGNOSIS2005                        1.122e+00  8.909e-01   0.58720    2.1455
YEAR_OF_DIAGNOSIS2006                        1.155e+00  8.658e-01   0.58672    2.2735
YEAR_OF_DIAGNOSIS2007                        1.551e+00  6.448e-01   0.80674    2.9814
YEAR_OF_DIAGNOSIS2008                        9.092e-01  1.100e+00   0.46056    1.7950
YEAR_OF_DIAGNOSIS2009                        1.185e+00  8.436e-01   0.58603    2.3976
YEAR_OF_DIAGNOSIS2010                        1.479e+00  6.763e-01   0.71580    3.0549
YEAR_OF_DIAGNOSIS2011                        1.422e+00  7.030e-01   0.69791    2.8992
YEAR_OF_DIAGNOSIS2012                        1.191e+00  8.395e-01   0.52154    2.7209
YEAR_OF_DIAGNOSIS2013                        1.878e+00  5.325e-01   0.90560    3.8945
YEAR_OF_DIAGNOSIS2014                        1.206e+00  8.290e-01   0.50803    2.8642
YEAR_OF_DIAGNOSIS2015                        1.555e+00  6.432e-01   0.66909    3.6122
YEAR_OF_DIAGNOSIS2016                               NA         NA        NA        NA

Concordance= 0.77  (se = 0.021 )
Rsquare= 0.247   (max possible= 0.956 )
Likelihood ratio test= 260.3  on 51 df,   p=0
Wald test            = 238.1  on 51 df,   p=0
Score (logrank) test = 316.2  on 51 df,   p=0

Summary of Model

model_one %>%
        tidy(., exponentiate = TRUE) %>%
        select(term, estimate, conf.low, conf.high, p.value) %>%
        rename(Variable = term,
               Hazard_Ratio = estimate) %>%
        tbl_df %>%
        print(n = nrow(.))

Linear Regression

#only include rows with known treatment information, n = 82
data2 <- data %>% filter(SURGERY_YN != "Ukn" & RADIATION_YN != "Ukn"
                         & CHEMO_YN != "Ukn" & IMMUNO_YN != "Ukn")
# include only variables with data available for at least 75% cases 
# from the following set of variables of interest:
## FACILITY_TYPE_F + FACILITY_GEOGRAPHY + CROWFLY + 
##                 DX_STAGING_PROC_DAYS + 
##                 CHEMO_YN + RADIATION_YN + SURGERY_YN + IMMUNO_YN +
##                 AGE + SEX_F + RACE_F + HISPANIC + INSURANCE_F + INCOME_F + 
##                 EDUCATION_F + YEAR_OF_DIAGNOSIS + SITE_TEXT + GRADE_F
length(which(is.na(data2$GRADE_F))) / nrow(data2)
[1] 0
# excluded the following in this analysis due to missing data: 
#  DX_STAGING_PROC_DAYS, GRADE_F (mostly unknowns)
fit_surv <- lm(DX_LASTCONTACT_DEATH_MONTHS ~
                 FACILITY_TYPE_F + FACILITY_LOCATION_F + CROWFLY + 
                 CHEMO_YN + RADIATION_YN + SURGERY_YN + 
                 AGE_F + RACE_F + 
                 EDUCATION_F + YEAR_OF_DIAGNOSIS + SITE_TEXT,
   data = data2)
summary(fit_surv) # R^2 = 0.3936, p < 2.2e-16

Call:
lm(formula = DX_LASTCONTACT_DEATH_MONTHS ~ FACILITY_TYPE_F + 
    FACILITY_LOCATION_F + CROWFLY + CHEMO_YN + RADIATION_YN + 
    SURGERY_YN + AGE_F + RACE_F + EDUCATION_F + YEAR_OF_DIAGNOSIS + 
    SITE_TEXT, data = data2)

Residuals:
     Min       1Q   Median       3Q      Max 
-102.829  -15.488    4.232   19.088   77.755 

Coefficients:
                                                           Estimate Std. Error t value Pr(>|t|)    
(Intercept)                                              131.854584  23.865368   5.525 4.02e-08 ***
FACILITY_TYPE_FComprehensive Comm Ca Program              -7.103358   5.977332  -1.188 0.234911    
FACILITY_TYPE_FAcademic/Research Program                  -4.195471   5.933651  -0.707 0.479660    
FACILITY_TYPE_FIntegrated Network Ca Program             -10.256613   6.253279  -1.640 0.101219    
FACILITY_LOCATION_FMiddle Atlantic                        -2.552452   4.740787  -0.538 0.590396    
FACILITY_LOCATION_FSouth Atlantic                         -0.443608   4.734474  -0.094 0.925365    
FACILITY_LOCATION_FEast North Central                     -1.026765   4.760021  -0.216 0.829253    
FACILITY_LOCATION_FEast South Central                     -2.959597   5.582712  -0.530 0.596113    
FACILITY_LOCATION_FWest North Central                     -8.461496   5.048246  -1.676 0.093967 .  
FACILITY_LOCATION_FWest South Central                     -6.430030   5.428310  -1.185 0.236429    
FACILITY_LOCATION_FMountain                               -2.164771   5.657767  -0.383 0.702068    
FACILITY_LOCATION_FPacific                                -2.002885   4.990274  -0.401 0.688226    
CROWFLY                                                   -0.011262   0.007009  -1.607 0.108328    
CHEMO_YNYes                                              -20.914084   6.075051  -3.443 0.000595 ***
RADIATION_YNYes                                            3.326517   4.303344   0.773 0.439666    
SURGERY_YNYes                                              9.653116   3.113572   3.100 0.001977 ** 
AGE_F(54,64]                                              -0.614832   3.486502  -0.176 0.860051    
AGE_F(64,74]                                              -4.284352   3.279879  -1.306 0.191710    
AGE_F(74,100]                                            -13.075191   3.208060  -4.076 4.88e-05 ***
RACE_FBlack                                               -4.962239   6.526717  -0.760 0.447223    
RACE_FOther/Unk                                           -2.842179   5.637883  -0.504 0.614266    
RACE_FAsian                                               -6.496482   4.225199  -1.538 0.124413    
EDUCATION_F13 - 20.9%                                     -1.011504   3.149863  -0.321 0.748169    
EDUCATION_F7 - 12.9%                                      -3.441575   3.029905  -1.136 0.256232    
EDUCATION_FLess than 7%                                   -0.012430   3.117930  -0.004 0.996820    
YEAR_OF_DIAGNOSIS2005                                     -8.308035   4.984327  -1.667 0.095802 .  
YEAR_OF_DIAGNOSIS2006                                    -19.318756   4.867251  -3.969 7.63e-05 ***
YEAR_OF_DIAGNOSIS2007                                    -16.897732   4.757487  -3.552 0.000397 ***
YEAR_OF_DIAGNOSIS2008                                    -23.336003   4.621805  -5.049 5.11e-07 ***
YEAR_OF_DIAGNOSIS2009                                    -27.089881   4.714740  -5.746 1.15e-08 ***
YEAR_OF_DIAGNOSIS2010                                    -34.902222   4.728322  -7.382 2.87e-13 ***
YEAR_OF_DIAGNOSIS2011                                    -42.418639   4.610705  -9.200  < 2e-16 ***
YEAR_OF_DIAGNOSIS2012                                    -50.982697   4.822674 -10.571  < 2e-16 ***
YEAR_OF_DIAGNOSIS2013                                    -58.505775   4.652914 -12.574  < 2e-16 ***
YEAR_OF_DIAGNOSIS2014                                    -69.662016   4.659361 -14.951  < 2e-16 ***
YEAR_OF_DIAGNOSIS2015                                    -76.503760   4.456774 -17.166  < 2e-16 ***
SITE_TEXTC44.3 Skin of ear and unspecified parts of face -60.655519  31.512899  -1.925 0.054486 .  
SITE_TEXTC44.4 Skin of scalp and neck                    -37.220527  28.815055  -1.292 0.196701    
SITE_TEXTC44.5 Skin of trunk                             -30.878575  22.269634  -1.387 0.165821    
SITE_TEXTC44.6 Skin of upper limb and shoulder           -30.048077  25.222001  -1.191 0.233748    
SITE_TEXTC44.7 Skin of lower limb and hip                -19.830117  24.316356  -0.816 0.414940    
SITE_TEXTC44.8 Overlapping lesion of skin                -30.786391  24.301859  -1.267 0.205454    
SITE_TEXTC44.9 Skin, NOS                                 -49.708925  23.611822  -2.105 0.035471 *  
SITE_TEXTC51.0 Labium majus                              -21.184294  22.441771  -0.944 0.345371    
SITE_TEXTC51.1 Labium minus                              -26.281435  23.689139  -1.109 0.267461    
SITE_TEXTC51.2 Clitoris                                   -4.558127  31.287977  -0.146 0.884195    
SITE_TEXTC51.8 Overlapping lesion of vulva               -24.592069  22.560158  -1.090 0.275897    
SITE_TEXTC51.9 Vulva, NOS                                -24.688061  22.220465  -1.111 0.266763    
SITE_TEXTC52.9 Vagina, NOS                               -13.093042  38.452320  -0.341 0.733537    
SITE_TEXTC60.2 Body of penis                              -6.695398  38.510895  -0.174 0.862006    
SITE_TEXTC60.8 Overlapping lesion of penis                 4.032756  31.486719   0.128 0.898108    
SITE_TEXTC60.9 Penis                                     -21.615462  23.179423  -0.933 0.351246    
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 31.01 on 1234 degrees of freedom
  (19 observations deleted due to missingness)
Multiple R-squared:  0.4172,    Adjusted R-squared:  0.3931 
F-statistic: 17.32 on 51 and 1234 DF,  p-value: < 2.2e-16
# the following variables were excluded to 
# improve the R-squared of the regression (initially R^2 = 0.3931):
# INCOME_F + INSURANCE_F + HISPANIC +  SEX_F + IMMUNO_YN +

Prediction Logistic Regression Models

Surgery

no_Ukns <- data2 %>%
  droplevels() %>% 
  mutate(SURGERY_YN = as.logical(SURGERY_YN))
# excluded the following in this analysis due to missing data: 
#  DX_STAGING_PROC_DAYS, GRADE_F (mostly unknowns)
fit_surg <- glm(SURG_TF ~ 
                 FACILITY_TYPE_F + FACILITY_LOCATION_F + 
                 CHEMO_YN + RADIATION_YN + IMMUNO_YN +
                 AGE_F + SEX_F + RACE_F + HISPANIC + INSURANCE_F + INCOME_F + 
                 EDUCATION_F + YEAR_OF_DIAGNOSIS + SITE_TEXT,
   data = no_Ukns)
summary(fit_surg)

Call:
glm(formula = SURG_TF ~ FACILITY_TYPE_F + FACILITY_LOCATION_F + 
    CHEMO_YN + RADIATION_YN + IMMUNO_YN + AGE_F + SEX_F + RACE_F + 
    HISPANIC + INSURANCE_F + INCOME_F + EDUCATION_F + YEAR_OF_DIAGNOSIS + 
    SITE_TEXT, data = no_Ukns)

Deviance Residuals: 
     Min        1Q    Median        3Q       Max  
-1.08847  -0.01519   0.03628   0.08972   0.85785  

Coefficients:
                                                           Estimate Std. Error t value Pr(>|t|)    
(Intercept)                                               0.7992782  0.1994927   4.007 6.53e-05 ***
FACILITY_TYPE_FComprehensive Comm Ca Program              0.1691498  0.0506722   3.338 0.000869 ***
FACILITY_TYPE_FAcademic/Research Program                  0.2009385  0.0502228   4.001 6.69e-05 ***
FACILITY_TYPE_FIntegrated Network Ca Program              0.1956925  0.0529954   3.693 0.000232 ***
FACILITY_LOCATION_FMiddle Atlantic                        0.0591566  0.0398579   1.484 0.138016    
FACILITY_LOCATION_FSouth Atlantic                         0.0702214  0.0399139   1.759 0.078773 .  
FACILITY_LOCATION_FEast North Central                     0.0592271  0.0403392   1.468 0.142299    
FACILITY_LOCATION_FEast South Central                     0.0256378  0.0474329   0.541 0.588946    
FACILITY_LOCATION_FWest North Central                     0.0487593  0.0430655   1.132 0.257767    
FACILITY_LOCATION_FWest South Central                     0.0659128  0.0460796   1.430 0.152854    
FACILITY_LOCATION_FMountain                               0.0190648  0.0478983   0.398 0.690680    
FACILITY_LOCATION_FPacific                                0.0372704  0.0421142   0.885 0.376339    
CHEMO_YNYes                                              -0.2620715  0.0506199  -5.177 2.63e-07 ***
RADIATION_YNYes                                          -0.5279098  0.0336209 -15.702  < 2e-16 ***
IMMUNO_YNYes                                             -0.5074473  0.0335637 -15.119  < 2e-16 ***
AGE_F(54,64]                                              0.0098455  0.0295117   0.334 0.738729    
AGE_F(64,74]                                             -0.0178567  0.0314900  -0.567 0.570779    
AGE_F(74,100]                                            -0.0836784  0.0312884  -2.674 0.007586 ** 
SEX_FFemale                                               0.0080008  0.0319854   0.250 0.802521    
RACE_FBlack                                              -0.0860855  0.0550881  -1.563 0.118384    
RACE_FOther/Unk                                          -0.0399660  0.0497251  -0.804 0.421704    
RACE_FAsian                                               0.0261528  0.0362208   0.722 0.470409    
HISPANICYes                                               0.0093300  0.0427723   0.218 0.827363    
HISPANICUnknown                                           0.0316369  0.0351836   0.899 0.368725    
INSURANCE_FNone                                           0.0425970  0.0583273   0.730 0.465340    
INSURANCE_FMedicaid                                      -0.1619355  0.0603065  -2.685 0.007346 ** 
INSURANCE_FMedicare                                       0.0094878  0.0216824   0.438 0.661768    
INSURANCE_FOther Government                               0.0951770  0.1010802   0.942 0.346584    
INSURANCE_FUnknown                                       -0.1587164  0.0727948  -2.180 0.029423 *  
INCOME_F$38,000 - $47,999                                 0.0299341  0.0288804   1.036 0.300180    
INCOME_F$48,000 - $62,999                                 0.0060796  0.0297987   0.204 0.838369    
INCOME_F$63,000 +                                        -0.0145544  0.0328391  -0.443 0.657697    
EDUCATION_F13 - 20.9%                                     0.0210215  0.0281922   0.746 0.456023    
EDUCATION_F7 - 12.9%                                      0.0072040  0.0291652   0.247 0.804944    
EDUCATION_FLess than 7%                                   0.0413905  0.0329067   1.258 0.208700    
YEAR_OF_DIAGNOSIS2005                                    -0.0280056  0.0419033  -0.668 0.504044    
YEAR_OF_DIAGNOSIS2006                                     0.0209031  0.0411039   0.509 0.611165    
YEAR_OF_DIAGNOSIS2007                                    -0.0396419  0.0400932  -0.989 0.322985    
YEAR_OF_DIAGNOSIS2008                                     0.0337970  0.0389393   0.868 0.385597    
YEAR_OF_DIAGNOSIS2009                                    -0.0167611  0.0397162  -0.422 0.673084    
YEAR_OF_DIAGNOSIS2010                                     0.0131670  0.0400078   0.329 0.742129    
YEAR_OF_DIAGNOSIS2011                                    -0.0198470  0.0390272  -0.509 0.611165    
YEAR_OF_DIAGNOSIS2012                                    -0.0103101  0.0407652  -0.253 0.800378    
YEAR_OF_DIAGNOSIS2013                                    -0.0349070  0.0393181  -0.888 0.374817    
YEAR_OF_DIAGNOSIS2014                                    -0.0420014  0.0393561  -1.067 0.286085    
YEAR_OF_DIAGNOSIS2015                                    -0.0239495  0.0377160  -0.635 0.525550    
SITE_TEXTC44.3 Skin of ear and unspecified parts of face  0.0491696  0.2649569   0.186 0.852808    
SITE_TEXTC44.4 Skin of scalp and neck                    -0.0003941  0.2418815  -0.002 0.998700    
SITE_TEXTC44.5 Skin of trunk                             -0.1006232  0.1872581  -0.537 0.591123    
SITE_TEXTC44.6 Skin of upper limb and shoulder           -0.1727345  0.2115522  -0.817 0.414367    
SITE_TEXTC44.7 Skin of lower limb and hip                -0.0938265  0.2040459  -0.460 0.645720    
SITE_TEXTC44.8 Overlapping lesion of skin                -0.1478607  0.2039516  -0.725 0.468603    
SITE_TEXTC44.9 Skin, NOS                                 -0.1832964  0.1983797  -0.924 0.355685    
SITE_TEXTC51.0 Labium majus                              -0.0689024  0.1912244  -0.360 0.718668    
SITE_TEXTC51.1 Labium minus                              -0.1397233  0.2014844  -0.693 0.488146    
SITE_TEXTC51.2 Clitoris                                   0.0597863  0.2641318   0.226 0.820967    
SITE_TEXTC51.8 Overlapping lesion of vulva               -0.0600840  0.1921915  -0.313 0.754618    
SITE_TEXTC51.9 Vulva, NOS                                -0.0639352  0.1893800  -0.338 0.735721    
SITE_TEXTC52.9 Vagina, NOS                                0.0484063  0.3242189   0.149 0.881341    
SITE_TEXTC60.2 Body of penis                             -0.0400083  0.3232956  -0.124 0.901532    
SITE_TEXTC60.8 Overlapping lesion of penis                0.1053808  0.2641287   0.399 0.689981    
SITE_TEXTC60.9 Penis                                      0.0027874  0.1945377   0.014 0.988571    
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

(Dispersion parameter for gaussian family taken to be 0.06750295)

    Null deviance: 134.797  on 1285  degrees of freedom
Residual deviance:  82.624  on 1224  degrees of freedom
  (19 observations deleted due to missingness)
AIC: 245.44

Number of Fisher Scoring iterations: 2
# the following variables were excluded to 
# improve the R-squared of the regression (initially residual = 82.534):
# none
exp(cbind("Odds ratio" = coef(fit_surg), confint.default(fit_surg, level = 0.95)))
                                                         Odds ratio     2.5 %    97.5 %
(Intercept)                                               2.2239350 1.5042279 3.2879905
FACILITY_TYPE_FComprehensive Comm Ca Program              1.1842975 1.0723302 1.3079558
FACILITY_TYPE_FAcademic/Research Program                  1.2225496 1.1079414 1.3490131
FACILITY_TYPE_FIntegrated Network Ca Program              1.2161529 1.0961712 1.3492672
FACILITY_LOCATION_FMiddle Atlantic                        1.0609414 0.9812152 1.1471455
FACILITY_LOCATION_FSouth Atlantic                         1.0727456 0.9920235 1.1600362
FACILITY_LOCATION_FEast North Central                     1.0610162 0.9803593 1.1483090
FACILITY_LOCATION_FEast South Central                     1.0259692 0.9348876 1.1259245
FACILITY_LOCATION_FWest North Central                     1.0499676 0.9649804 1.1424398
FACILITY_LOCATION_FWest South Central                     1.0681336 0.9758937 1.1690919
FACILITY_LOCATION_FMountain                               1.0192477 0.9279159 1.1195690
FACILITY_LOCATION_FPacific                                1.0379736 0.9557376 1.1272856
CHEMO_YNYes                                               0.7694560 0.6967805 0.8497116
RADIATION_YNYes                                           0.5898365 0.5522218 0.6300134
IMMUNO_YNYes                                              0.6020304 0.5637012 0.6429658
AGE_F(54,64]                                              1.0098941 0.9531373 1.0700306
AGE_F(64,74]                                              0.9823018 0.9235078 1.0448389
AGE_F(74,100]                                             0.9197270 0.8650201 0.9778938
SEX_FFemale                                               1.0080329 0.9467792 1.0732495
RACE_FBlack                                               0.9175158 0.8236117 1.0221263
RACE_FOther/Unk                                           0.9608221 0.8715993 1.0591783
RACE_FAsian                                               1.0264978 0.9561517 1.1020194
HISPANICYes                                               1.0093736 0.9282054 1.0976397
HISPANICUnknown                                           1.0321427 0.9633663 1.1058292
INSURANCE_FNone                                           1.0435173 0.9307893 1.1698978
INSURANCE_FMedicaid                                       0.8504960 0.7556824 0.9572058
INSURANCE_FMedicare                                       1.0095329 0.9675298 1.0533595
INSURANCE_FOther Government                               1.0998535 0.9021842 1.3408325
INSURANCE_FUnknown                                        0.8532383 0.7397881 0.9840867
INCOME_F$38,000 - $47,999                                 1.0303866 0.9736822 1.0903934
INCOME_F$48,000 - $62,999                                 1.0060982 0.9490207 1.0666085
INCOME_F$63,000 +                                         0.9855510 0.9241157 1.0510705
EDUCATION_F13 - 20.9%                                     1.0212440 0.9663451 1.0792617
EDUCATION_F7 - 12.9%                                      1.0072300 0.9512687 1.0664834
EDUCATION_FLess than 7%                                   1.0422590 0.9771593 1.1116957
YEAR_OF_DIAGNOSIS2005                                     0.9723829 0.8957136 1.0556149
YEAR_OF_DIAGNOSIS2006                                     1.0211231 0.9420856 1.1067916
YEAR_OF_DIAGNOSIS2007                                     0.9611336 0.8884978 1.0397075
YEAR_OF_DIAGNOSIS2008                                     1.0343746 0.9583688 1.1164082
YEAR_OF_DIAGNOSIS2009                                     0.9833786 0.9097336 1.0629854
YEAR_OF_DIAGNOSIS2010                                     1.0132540 0.9368362 1.0959053
YEAR_OF_DIAGNOSIS2011                                     0.9803487 0.9081562 1.0582800
YEAR_OF_DIAGNOSIS2012                                     0.9897429 0.9137407 1.0720667
YEAR_OF_DIAGNOSIS2013                                     0.9656952 0.8940720 1.0430561
YEAR_OF_DIAGNOSIS2014                                     0.9588684 0.8876853 1.0357597
YEAR_OF_DIAGNOSIS2015                                     0.9763351 0.9067655 1.0512422
SITE_TEXTC44.3 Skin of ear and unspecified parts of face  1.0503985 0.6249170 1.7655736
SITE_TEXTC44.4 Skin of scalp and neck                     0.9996060 0.6222129 1.6059008
SITE_TEXTC44.5 Skin of trunk                              0.9042737 0.6264776 1.3052517
SITE_TEXTC44.6 Skin of upper limb and shoulder            0.8413610 0.5557876 1.2736671
SITE_TEXTC44.7 Skin of lower limb and hip                 0.9104407 0.6103339 1.3581129
SITE_TEXTC44.8 Overlapping lesion of skin                 0.8625513 0.5783371 1.2864380
SITE_TEXTC44.9 Skin, NOS                                  0.8325214 0.5643315 1.2281644
SITE_TEXTC51.0 Labium majus                               0.9334178 0.6416609 1.3578336
SITE_TEXTC51.1 Labium minus                               0.8695988 0.5858887 1.2906924
SITE_TEXTC51.2 Clitoris                                   1.0616097 0.6326091 1.7815346
SITE_TEXTC51.8 Overlapping lesion of vulva                0.9416854 0.6461185 1.3724594
SITE_TEXTC51.9 Vulva, NOS                                 0.9380658 0.6471914 1.3596712
SITE_TEXTC52.9 Vagina, NOS                                1.0495970 0.5559643 1.9815190
SITE_TEXTC60.2 Body of penis                              0.9607815 0.5098412 1.8105658
SITE_TEXTC60.8 Overlapping lesion of penis                1.1111336 0.6621243 1.8646316
SITE_TEXTC60.9 Penis                                      1.0027912 0.6848883 1.4682545

Metastasis at Time of Diagnosis

fit_mets <- glm(mets_at_dx_F ~ 
                 FACILITY_TYPE_F + FACILITY_GEOGRAPHY + CROWFLY + 
                 AGE_F + SEX_F + RACE_F + HISPANIC + INSURANCE_F + INCOME_F + 
                 EDUCATION_F + YEAR_OF_DIAGNOSIS + SITE_TEXT,
   data = data)
# the following variables were excluded to 
# improve the R-squared of the regression (initially residual = 4.7169):
# 
summary(fit_mets)

Call:
glm(formula = mets_at_dx_F ~ FACILITY_TYPE_F + FACILITY_GEOGRAPHY + 
    CROWFLY + AGE_F + SEX_F + RACE_F + HISPANIC + INSURANCE_F + 
    INCOME_F + EDUCATION_F + YEAR_OF_DIAGNOSIS + SITE_TEXT, data = data)

Deviance Residuals: 
     Min        1Q    Median        3Q       Max  
-0.10474  -0.00900  -0.00140   0.00526   0.98148  

Coefficients:
                                                           Estimate Std. Error t value Pr(>|t|)   
(Intercept)                                              -2.246e-03  4.563e-02  -0.049  0.96076   
FACILITY_TYPE_FComprehensive Comm Ca Program              9.826e-03  1.150e-02   0.855  0.39297   
FACILITY_TYPE_FAcademic/Research Program                  4.835e-03  1.139e-02   0.424  0.67138   
FACILITY_TYPE_FIntegrated Network Ca Program              5.693e-03  1.202e-02   0.474  0.63590   
FACILITY_GEOGRAPHYSouth                                  -5.501e-03  5.120e-03  -1.074  0.28291   
FACILITY_GEOGRAPHYMidwest                                -2.534e-03  4.877e-03  -0.520  0.60344   
FACILITY_GEOGRAPHYWest                                   -4.009e-03  5.529e-03  -0.725  0.46853   
CROWFLY                                                  -7.566e-07  1.352e-05  -0.056  0.95539   
AGE_F(54,64]                                              1.886e-03  6.725e-03   0.281  0.77912   
AGE_F(64,74]                                              6.281e-03  7.180e-03   0.875  0.38186   
AGE_F(74,100]                                             6.812e-03  7.118e-03   0.957  0.33877   
SEX_FFemale                                              -1.828e-03  7.293e-03  -0.251  0.80217   
RACE_FBlack                                              -4.189e-04  1.254e-02  -0.033  0.97334   
RACE_FOther/Unk                                          -4.283e-03  1.120e-02  -0.382  0.70217   
RACE_FAsian                                               2.455e-02  8.083e-03   3.037  0.00244 **
HISPANICYes                                               1.957e-02  9.636e-03   2.031  0.04243 * 
HISPANICUnknown                                           1.026e-03  7.821e-03   0.131  0.89568   
INSURANCE_FNone                                          -9.324e-03  1.353e-02  -0.689  0.49098   
INSURANCE_FMedicaid                                      -1.555e-02  1.365e-02  -1.140  0.25469   
INSURANCE_FMedicare                                      -5.955e-03  4.991e-03  -1.193  0.23304   
INSURANCE_FOther Government                              -1.559e-03  2.197e-02  -0.071  0.94344   
INSURANCE_FUnknown                                       -6.928e-03  1.633e-02  -0.424  0.67146   
INCOME_F$38,000 - $47,999                                 6.589e-03  6.394e-03   1.031  0.30293   
INCOME_F$48,000 - $62,999                                 1.204e-02  6.619e-03   1.819  0.06917 . 
INCOME_F$63,000 +                                         1.702e-02  7.288e-03   2.336  0.01966 * 
EDUCATION_F13 - 20.9%                                    -8.845e-03  6.385e-03  -1.385  0.16621   
EDUCATION_F7 - 12.9%                                     -1.294e-02  6.528e-03  -1.983  0.04759 * 
EDUCATION_FLess than 7%                                  -2.165e-02  7.338e-03  -2.951  0.00323 **
YEAR_OF_DIAGNOSIS2005                                    -1.998e-03  9.430e-03  -0.212  0.83220   
YEAR_OF_DIAGNOSIS2006                                    -4.035e-03  9.234e-03  -0.437  0.66219   
YEAR_OF_DIAGNOSIS2007                                    -1.436e-03  8.973e-03  -0.160  0.87288   
YEAR_OF_DIAGNOSIS2008                                    -3.889e-04  8.802e-03  -0.044  0.96476   
YEAR_OF_DIAGNOSIS2009                                    -1.462e-03  9.001e-03  -0.162  0.87096   
YEAR_OF_DIAGNOSIS2010                                     1.291e-02  8.943e-03   1.444  0.14902   
YEAR_OF_DIAGNOSIS2011                                    -3.060e-03  8.794e-03  -0.348  0.72789   
YEAR_OF_DIAGNOSIS2012                                    -2.169e-03  9.182e-03  -0.236  0.81329   
YEAR_OF_DIAGNOSIS2013                                     6.084e-03  8.898e-03   0.684  0.49428   
YEAR_OF_DIAGNOSIS2014                                     3.822e-03  8.889e-03   0.430  0.66729   
YEAR_OF_DIAGNOSIS2015                                     2.622e-03  8.466e-03   0.310  0.75687   
SITE_TEXTC44.3 Skin of ear and unspecified parts of face -9.680e-03  6.130e-02  -0.158  0.87455   
SITE_TEXTC44.4 Skin of scalp and neck                    -1.244e-03  5.598e-02  -0.022  0.98227   
SITE_TEXTC44.5 Skin of trunk                              9.195e-03  4.344e-02   0.212  0.83240   
SITE_TEXTC44.6 Skin of upper limb and shoulder           -6.175e-03  4.906e-02  -0.126  0.89984   
SITE_TEXTC44.7 Skin of lower limb and hip                 7.838e-02  4.667e-02   1.679  0.09331 . 
SITE_TEXTC44.8 Overlapping lesion of skin                -3.863e-04  4.700e-02  -0.008  0.99344   
SITE_TEXTC44.9 Skin, NOS                                 -3.867e-03  4.561e-02  -0.085  0.93245   
SITE_TEXTC51.0 Labium majus                              -3.474e-03  4.430e-02  -0.078  0.93751   
SITE_TEXTC51.1 Labium minus                              -2.891e-03  4.677e-02  -0.062  0.95072   
SITE_TEXTC51.2 Clitoris                                  -4.121e-03  6.129e-02  -0.067  0.94640   
SITE_TEXTC51.8 Overlapping lesion of vulva               -1.170e-03  4.459e-02  -0.026  0.97908   
SITE_TEXTC51.9 Vulva, NOS                                -1.240e-04  4.393e-02  -0.003  0.99775   
SITE_TEXTC52.9 Vagina, NOS                               -4.835e-04  7.514e-02  -0.006  0.99487   
SITE_TEXTC60.2 Body of penis                             -2.164e-02  7.510e-02  -0.288  0.77323   
SITE_TEXTC60.8 Overlapping lesion of penis               -1.678e-02  6.123e-02  -0.274  0.78405   
SITE_TEXTC60.9 Penis                                     -8.980e-03  4.515e-02  -0.199  0.84238   
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

(Dispersion parameter for gaussian family taken to be 0.003667907)

    Null deviance: 4.9814  on 1340  degrees of freedom
Residual deviance: 4.7169  on 1286  degrees of freedom
  (19 observations deleted due to missingness)
AIC: -3659.1

Number of Fisher Scoring iterations: 2
exp(cbind("Odds ratio" = coef(fit_mets), confint.default(fit_surg, level = 0.95)))
number of rows of result is not a multiple of vector length (arg 1)
                                                         Odds ratio     2.5 %    97.5 %
(Intercept)                                               0.9977569 1.5457663 3.3703132
FACILITY_TYPE_FComprehensive Comm Ca Program              1.0098744 1.0741650 1.3098309
FACILITY_TYPE_FAcademic/Research Program                  1.0048465 1.1098380 1.3511222
FACILITY_TYPE_FIntegrated Network Ca Program              1.0057094 1.0984706 1.3514208
FACILITY_LOCATION_FMiddle Atlantic                        0.9945144 0.9823566 1.1483911
FACILITY_LOCATION_FSouth Atlantic                         0.9974693 0.9936150 1.1618048
FACILITY_LOCATION_FEast North Central                     0.9959987 0.9836801 1.1519857
FACILITY_LOCATION_FEast South Central                     0.9999992 0.9346984 1.1250120
FACILITY_LOCATION_FWest North Central                     1.0018882 0.9712046 1.1490674
FACILITY_LOCATION_FWest South Central                     1.0063008 0.9773330 1.1704730
FACILITY_LOCATION_FMountain                               1.0068353 0.9298858 1.1215533
FACILITY_LOCATION_FPacific                                0.9981740 0.9548599 1.1253096
CHEMO_YNYes                                               0.9995812 0.6965232 0.8493930
RADIATION_YNYes                                           0.9957263 0.5520826 0.6298419
IMMUNO_YNYes                                              1.0248521 0.5627377 0.6417435
AGE_F(54,64]                                              1.0197677 0.9505095 1.0667637
AGE_F(64,74]                                              1.0010262 0.9216843 1.0426608
AGE_F(74,100]                                             0.9907196 0.8626780 0.9749483
SEX_FFemale                                               0.9845696 0.9468771 1.0733025
RACE_FBlack                                               0.9940631 0.8225687 1.0207841
RACE_FOther/Unk                                           0.9984423 0.8705686 1.0572071
RACE_FAsian                                               0.9930964 0.9505355 1.0941834
HISPANICYes                                               1.0066112 0.9237778 1.0905714
HISPANICUnknown                                           1.0121116 0.9614583 1.1035687
INSURANCE_FNone                                           1.0171696 0.9316619 1.1707760
INSURANCE_FMedicaid                                       0.9911936 0.7538137 0.9545322
INSURANCE_FMedicare                                       0.9871386 0.9686253 1.0544896
INSURANCE_FOther Government                               0.9785821 0.9026097 1.3414528
INSURANCE_FUnknown                                        0.9980035 0.7395891 0.9836876
INCOME_F$38,000 - $47,999                                 0.9959727 0.9825787 1.0931054
INCOME_F$48,000 - $62,999                                 0.9985652 0.9615730 1.0686626
INCOME_F$63,000 +                                         0.9996112 0.9550985 1.0596392
YEAR_OF_DIAGNOSIS2005                                     0.9985387 0.8922150 1.0512723
YEAR_OF_DIAGNOSIS2006                                     1.0129963 0.9375456 1.1007709
YEAR_OF_DIAGNOSIS2007                                     0.9969444 0.8861404 1.0366234
YEAR_OF_DIAGNOSIS2008                                     0.9978333 0.9562699 1.1134542
YEAR_OF_DIAGNOSIS2009                                     1.0061024 0.9081843 1.0610225
YEAR_OF_DIAGNOSIS2010                                     1.0038294 0.9330281 1.0911199
YEAR_OF_DIAGNOSIS2011                                     1.0026251 0.9044774 1.0535009
YEAR_OF_DIAGNOSIS2012                                     0.9903663 0.9106737 1.0680287
YEAR_OF_DIAGNOSIS2013                                     0.9987567 0.8911863 1.0394772
YEAR_OF_DIAGNOSIS2014                                     1.0092373 0.8854901 1.0330336
YEAR_OF_DIAGNOSIS2015                                     0.9938436 0.9050425 1.0487749
SITE_TEXTC44.3 Skin of ear and unspecified parts of face  1.0815320 0.6161945 1.7399009
SITE_TEXTC44.4 Skin of scalp and neck                     0.9996138 0.6102982 1.5723573
SITE_TEXTC44.5 Skin of trunk                              0.9961408 0.6172570 1.2847405
SITE_TEXTC44.6 Skin of upper limb and shoulder            0.9965321 0.5458001 1.2492437
SITE_TEXTC44.7 Skin of lower limb and hip                 0.9971129 0.6042699 1.3443356
SITE_TEXTC44.8 Overlapping lesion of skin                 0.9958877 0.5711006 1.2697031
SITE_TEXTC44.9 Skin, NOS                                  0.9988310 0.5517221 1.1981104
SITE_TEXTC51.0 Labium majus                               0.9998760 0.6309594 1.3335806
SITE_TEXTC51.1 Labium minus                               0.9995167 0.5785708 1.2737706
SITE_TEXTC51.2 Clitoris                                   0.9785884 0.6231168 1.7534793
SITE_TEXTC51.8 Overlapping lesion of vulva                0.9833584 0.6369175 1.3516725
SITE_TEXTC51.9 Vulva, NOS                                 0.9910605 0.6378399 1.3385614
SITE_TEXTC52.9 Vagina, NOS                                0.9977569 0.5552974 1.9789396
SITE_TEXTC60.2 Body of penis                              1.0098744 0.5127871 1.8205651
SITE_TEXTC60.8 Overlapping lesion of penis                1.0048465 0.6513181 1.8326073
SITE_TEXTC60.9 Penis                                      1.0057094 0.6757319 1.4478081
---
title: "Extramammary Paget Disease - Review of the NCDB"
author: "Ramie Fathy"
date: "11/05/2019"
output:
  html_notebook:
    theme: united
    toc: yes
    toc_float: yes
  html_document:
    toc: yes
---



```{r, echo=FALSE, warning=FALSE, message=FALSE}

library("ggplot2")
library("dplyr")
library("tidyr")
library("knitr")
library("tableone")
library("forcats")
library("survival")
library("npsurv")
library("broom")
library("tibble")
library("readr")
library("survminer")
library("stringr")


knitr::opts_chunk$set(echo=TRUE, warning=FALSE, message=TRUE)
'%!in%' <- function(x,y)!('%in%'(x,y))
```

```{r}
p_table <- function(tab_data, ...) {
  tab_data_2 <- deparse(substitute(tab_data))
  
  table_p <- do.call(CreateTableOne, 
                     list(data = as.name(tab_data_2), includeNA = TRUE, ...))
  table_p_out <- print(table_p,
                       showAllLevels = TRUE,
                       printToggle = FALSE)
  kable(table_p_out,
        align = "c")
}
```

```{r}
uni_var <- function(test_var, data_imp) {

                
        cat("_________________________________________________")
        cat("\n")
        cat("   \n##", test_var)
        cat("\n")
        cat("_________________________________________________")
        cat("\n")

        
        f <- as.formula(paste("Surv(DX_LASTCONTACT_DEATH_MONTHS, PUF_VITAL_STATUS == 0)",
                              as.name(test_var),
                              sep = " ~ " ))
        
        data_imp_2 <- deparse(substitute(data_imp))

        km_fit <- do.call("survfit", list(formula = f, data = as.name(data_imp_2)))

        print(km_fit)
        cat("\n")

        print(summary(km_fit, times = c(12, 24, 36, 48, 60, 120)))
        cat("\n")


        cat("\n")
        cat("\n")
        cat("   \n## Univariable Cox Proportional Hazard Model for: ", test_var)
        cat("\n")
        cat("\n")


        n_levels <- nlevels(data_imp[[test_var]])

        if(n_levels == 1){
                print("Only one level, no Cox model performed")
                cat("\n")

        } else {


                cox_fit <- do.call("coxph", list(formula = f, data = as.name(data_imp_2)))

                print(summary(cox_fit))
                cat("\n")
                
                do.call("ggforest",
                         list(model = cox_fit, data = as.name(data_imp_2)))


        }

        cat("\n")
        cat("\n")
        cat("\n")
        cat("   \n## Unadjusted Kaplan Meier Overall Survival Curve for: ", test_var)


        p <- do.call("ggsurvplot",
                     list(fit = km_fit, data = as.name(data_imp_2),
                          palette = "jco", censor = FALSE, legend = "right",
                          linetype = "strata", xlab = "Time (Months)"))

        print(p)

}

```

```{r}
f_plot <- function(test_var, data_imp){

                
        cat("_________________________________________________")
        cat("\n")
        cat("   \n##", test_var)
        cat("\n")
        cat("_________________________________________________")
        cat("\n")

        
        f <- as.formula(paste(as.name(test_var),
                              "AGE + SEX + T_SIZE + FACILITY_TYPE_F + FACILITY_LOCATION_F + YEAR_OF_DIAGNOSIS",
                              sep = " ~ " ))
        
        data_imp_2 <- deparse(substitute(data_imp))
        
        fit_fn <- do.call("glm", 
                       list(formula = f, 
                            data = as.name(data_imp_2), 
                            family = "binomial"))
        
        print(summary(fit_fn))
        
        or <- as.data.frame(exp(coefficients(fit_fn)))
        or$Variable <- rownames(or)
        rownames(or) <- c()
        names(or) <- c('OddsRatio', 'Variable')

        ci <- as.data.frame(exp(confint(fit_fn)))
        ci$Variable <- rownames(ci)
        rownames(ci) <- c()

        p_val_list <- tidy(fit_fn) %>%
        select(term, p.value) %>%
        rename(Variable = term) %>%
        mutate(p.value = round(p.value, 4))
        p_val_list$p.value <- as.character(p_val_list$p.value)
        p_val_list$p.value[p_val_list$p.value == "0"] <- "< 0.0001"

        all <- full_join(or, ci, by = 'Variable')
        all <- full_join(all, p_val_list, by = "Variable")
        names(all) <- c('OddsRatio', 'Variable', 'Lower', 'Upper', "p_value")
        all <- na.omit(all)

        all <- all %>%
        filter(Variable != '(Intercept)') 


        text <- cbind(c("Variable", as.character(all$Variable)), 
              c("Odds Ratio", as.character(round(all$OddsRatio, 2))),
              c("Lower CI", as.character(round(all$Lower, 2))),
              c("Upper CI", as.character(round(all$Upper, 2))),
              c("p Value", all$p_value))


        forestplot(text, 
           mean = c(NA, all$OddsRatio), 
           lower = c(NA, all$Lower), 
           upper = c(NA, all$Upper), 
           new_page =   TRUE, zero = 1, 
           clip = c(0.1, 100),
           hrzl_lines = list("2" = gpar(col="#444444")),
           vertices = TRUE,
           graph.pos = 2,
           xlab = "Odds Ratio (log)",
           align = "c",
           txt_gp = fpTxtGp(cex = 0.7),
           xticks = getTicks(low = all$Lower,
                             high = all$Upper,
                             clip=c(-Inf, Inf),
                             exp=TRUE),
           boxsize = 0.1)
    
}
```

```{r chunk2, cache=TRUE, message=FALSE, warning=FALSE, results='hide'}
col.width <- c(37, 10, 1, 1, 3, 1, 2, 1, 2, 1, 1, 1, 1, 1, 1, 8, 2, 2, 2, 4, 4, 1, 4, 1, 1,
               1, 3, 2, 2, 8, 2, 5, 5, 5, 4, 5, 5, 5,4, 2, 1, 2, 1, 3, 1, 1, 1, 1, 1, 1, 3,
               3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 6, 8,
               8, 8, 2, 1, 1, 1, 1, 8, 1, 1, 8, 1, 1, 2, 2, 5, 2, 5, 3, 1, 3, 1, 8, 8, 2, 8,
               2, 8, 2, 2, 1, 8, 1, 1, 1, 1, 1, 8, 1, 2, 2, 2, 2, 2, 1, 1, 1, 2, 1, 3, 1, 1,
               1, 1, 1, 1, 1, 1, 1)

col.names.abr <- c("PUF_CASE_ID", "PUF_FACILITY_ID", "FACILITY_TYPE_CD", "FACILITY_LOCATION_CD",
                   "AGE", "SEX", "RACE", "SPANISH_HISPANIC_ORIGIN", "INSURANCE_STATUS",
                   "MED_INC_QUAR_00", "NO_HSD_QUAR_00", "UR_CD_03", "MED_INC_QUAR_12", "NO_HSD_QUAR_12",
                   "UR_CD_13", "CROWFLY", "CDCC_TOTAL_BEST", "SEQUENCE_NUMBER", "CLASS_OF_CASE",
                   "YEAR_OF_DIAGNOSIS", "PRIMARY_SITE", "LATERALITY", "HISTOLOGY", "BEHAVIOR", "GRADE",
                   "DIAGNOSTIC_CONFIRMATION", "TUMOR_SIZE", "REGIONAL_NODES_POSITIVE",
                   "REGIONAL_NODES_EXAMINED", "DX_STAGING_PROC_DAYS", "RX_SUMM_DXSTG_PROC", "TNM_CLIN_T",
                   "TNM_CLIN_N", "TNM_CLIN_M", "TNM_CLIN_STAGE_GROUP", "TNM_PATH_T", "TNM_PATH_N", "TNM_PATH_M",
                   "TNM_PATH_STAGE_GROUP", "TNM_EDITION_NUMBER", "ANALYTIC_STAGE_GROUP", "CS_METS_AT_DX",
                   "CS_METS_EVAL", "CS_EXTENSION", "CS_TUMOR_SIZEEXT_EVAL", "CS_METS_DX_BONE", "CS_METS_DX_BRAIN",
                   "CS_METS_DX_LIVER", "CS_METS_DX_LUNG", "LYMPH_VASCULAR_INVASION", "CS_SITESPECIFIC_FACTOR_1",
                   "CS_SITESPECIFIC_FACTOR_2", "CS_SITESPECIFIC_FACTOR_3", "CS_SITESPECIFIC_FACTOR_4",
                   "CS_SITESPECIFIC_FACTOR_5", "CS_SITESPECIFIC_FACTOR_6", "CS_SITESPECIFIC_FACTOR_7",
                   "CS_SITESPECIFIC_FACTOR_8", "CS_SITESPECIFIC_FACTOR_9", "CS_SITESPECIFIC_FACTOR_10",
                   "CS_SITESPECIFIC_FACTOR_11", "CS_SITESPECIFIC_FACTOR_12", "CS_SITESPECIFIC_FACTOR_13",
                   "CS_SITESPECIFIC_FACTOR_14", "CS_SITESPECIFIC_FACTOR_15", "CS_SITESPECIFIC_FACTOR_16",
                   "CS_SITESPECIFIC_FACTOR_17", "CS_SITESPECIFIC_FACTOR_18", "CS_SITESPECIFIC_FACTOR_19",
                   "CS_SITESPECIFIC_FACTOR_20", "CS_SITESPECIFIC_FACTOR_21", "CS_SITESPECIFIC_FACTOR_22",
                   "CS_SITESPECIFIC_FACTOR_23", "CS_SITESPECIFIC_FACTOR_24", "CS_SITESPECIFIC_FACTOR_25",
                   "CS_VERSION_LATEST", "DX_RX_STARTED_DAYS", "DX_SURG_STARTED_DAYS", "DX_DEFSURG_STARTED_DAYS",
                   "RX_SUMM_SURG_PRIM_SITE", "RX_HOSP_SURG_APPR_2010", "RX_SUMM_SURGICAL_MARGINS",
                   "RX_SUMM_SCOPE_REG_LN_SUR", "RX_SUMM_SURG_OTH_REGDIS", "SURG_DISCHARGE_DAYS", "READM_HOSP_30_DAYS",
                   "REASON_FOR_NO_SURGERY", "DX_RAD_STARTED_DAYS", "RX_SUMM_RADIATION", "RAD_LOCATION_OF_RX",
                   "RAD_TREAT_VOL", "RAD_REGIONAL_RX_MODALITY", "RAD_REGIONAL_DOSE_CGY", "RAD_BOOST_RX_MODALITY",
                   "RAD_BOOST_DOSE_CGY", "RAD_NUM_TREAT_VOL", "RX_SUMM_SURGRAD_SEQ", "RAD_ELAPSED_RX_DAYS",
                   "REASON_FOR_NO_RADIATION", "DX_SYSTEMIC_STARTED_DAYS", "DX_CHEMO_STARTED_DAYS", "RX_SUMM_CHEMO",
                   "DX_HORMONE_STARTED_DAYS", "RX_SUMM_HORMONE", "DX_IMMUNO_STARTED_DAYS", "RX_SUMM_IMMUNOTHERAPY",
                   "RX_SUMM_TRNSPLNT_ENDO", "RX_SUMM_SYSTEMIC_SUR_SEQ", "DX_OTHER_STARTED_DAYS", "RX_SUMM_OTHER",
                   "PALLIATIVE_CARE", "RX_SUMM_TREATMENT_STATUS", "PUF_30_DAY_MORT_CD", "PUF_90_DAY_MORT_CD",
                   "DX_LASTCONTACT_DEATH_MONTHS", "PUF_VITAL_STATUS", "RX_HOSP_SURG_PRIM_SITE", "RX_HOSP_CHEMO",
                   "RX_HOSP_IMMUNOTHERAPY", "RX_HOSP_HORMONE", "RX_HOSP_OTHER", "PUF_MULT_SOURCE", "REFERENCE_DATE_FLAG",
                   "RX_SUMM_SCOPE_REG_LN_2012", "RX_HOSP_DXSTG_PROC", "PALLIATIVE_CARE_HOSP", "TUMOR_SIZE_SUMMARY",
                   "METS_AT_DX_OTHER", "METS_AT_DX_DISTANT_LN", "METS_AT_DX_BONE", "METS_AT_DX_BRAIN",
                   "METS_AT_DX_LIVER", "METS_AT_DX_LUNG", "NO_HSD_QUAR_16", "MED_INC_QUAR_16", "MEDICAID_EXPN_CODE")



#Read in data for each subsite
lip <- read_fwf('NCDBPUF_Lip.3.2016.0.dat', 
                       fwf_widths(col.width, col_names = col.names.abr),
                       col_types = cols(.default = col_character()))

melanoma <- read_fwf('NCDBPUF_Melanoma.3.2016.0.dat', 
                       fwf_widths(col.width, col_names = col.names.abr),
                       col_types = cols(.default = col_character()))
                       
skin <- read_fwf('NCDBPUF_OtSkin.3.2016.0.dat', 
                       fwf_widths(col.width, col_names = col.names.abr),
                       col_types = cols(.default = col_character()))

hodgextr <- read_fwf('NCDBPUF_HodgExtr.3.2016.0.dat', 
                       fwf_widths(col.width, col_names = col.names.abr),
                       col_types = cols(.default = col_character()))

hodgndal <- read_fwf('NCDBPUF_HodgNdal.3.2016.0.dat', 
                       fwf_widths(col.width, col_names = col.names.abr),
                       col_types = cols(.default = col_character()))

NHLndal <- read_fwf('NCDBPUF_NHLNdal.3.2016.0.dat', 
                       fwf_widths(col.width, col_names = col.names.abr),
                       col_types = cols(.default = col_character()))

NHLextr <- read_fwf('NCDBPUF_NHLExtr.3.2016.0.dat', 
                       fwf_widths(col.width, col_names = col.names.abr),
                       col_types = cols(.default = col_character()))


breast <-  read_fwf('NCDBPUF_Breast.3.2016.0.dat', 
                       fwf_widths(col.width, col_names = col.names.abr),
                       col_types = cols(.default = col_character()))

vulva <-  read_fwf('NCDBPUF_Vulva.3.2016.0.dat', 
                       fwf_widths(col.width, col_names = col.names.abr),
                       col_types = cols(.default = col_character()))

vagina <-  read_fwf('NCDBPUF_Vagina.3.2016.0.dat', 
                       fwf_widths(col.width, col_names = col.names.abr),
                       col_types = cols(.default = col_character()))

penis <-  read_fwf('NCDBPUF_Penis.3.2016.0.dat', 
                       fwf_widths(col.width, col_names = col.names.abr),
                       col_types = cols(.default = col_character()))

otleuk <- read_fwf('NCDBPUF_OtLeuk.3.2016.0.dat', 
                       fwf_widths(col.width, col_names = col.names.abr),
                       col_types = cols(.default = col_character()))
  
otheracuteleuk  <- read_fwf('NCDBPUF_OtAcLeuk.3.2016.0.dat', 
                       fwf_widths(col.width, col_names = col.names.abr),
                       col_types = cols(.default = col_character()))
  
ALL  <- read_fwf('NCDBPUF_ALymLeuk.3.2016.0.dat', 
                       fwf_widths(col.width, col_names = col.names.abr),
                       col_types = cols(.default = col_character()))


#Combine data for all subsites
dat <- bind_rows(lip, melanoma, skin, hodgextr, hodgndal, NHLndal, breast, 
                 vulva, vagina, penis, NHLextr, otleuk, otheracuteleuk, ALL)

rm(lip, melanoma, skin, hodgextr, hodgndal, NHLndal, breast, vulva, vagina, 
   penis, NHLextr, otleuk, otheracuteleuk, ALL)

prim_site_text <- data_frame(PRIMARY_SITE = c(
#NHL sites
"C000", 
"C001", 
"C002", 
"C003", 
"C004", 
"C005", 
"C006", 
"C008",
"C009", 
"C019", 
"C020", 
"C021",
"C022", 
"C023", 
"C024", 
"C028", 
"C029",
"C030",
"C031",
"C039", 
"C040", 
"C041", 
"C048",
"C049", 
"C050", 
"C051", 
"C052", 
"C058", 
"C059",
"C060", 
"C061", 
"C062", 
"C068", 
"C069", 
"C079",  
"C098",
"C099",
"C111",
"C142",
"C300",
"C379",
"C420",
"C422",
"C424",

#skin/melanoma
                                 "C440", "C441", "C442", "C443", "C444", "C445",
                                 "C446", "C447", "C448", "C449",
                                 
                                 #breast - nipple
                                 "C500",
                                 
                                 #vagina/vulva
                                 "C510", "C511", "C512", "C518", "C519", "C529",
                                 
                                 #penis
                                 "C600", "C601", "C602", "C608", "C609", "C639",

"C770",
"C771",
"C772",
"C773",
"C774",
"C775",
"C778",
"C779"),

SITE_TEXT = c(
"C00.0 External Lip: Upper NOS",
"C00.1 External Lip: Lower NOS",
"C00.2 External Lip: NOS",
"C00.3 Lip: Upper Mucosa",
"C00.4 Lip: Lower Mucosa",
"C00.5 Lip: Mucosa NOS",
"C00.6 Lip: Commissure",
"C00.8 Lip: Overlapping",
"C00.9 Lip NOS",
"C01.9 Tongue: Base NOS",
"C02.0 Tongue: Dorsal NOS",
"C02.1 Tongue: Border, Tip",
"C02.2 Tongue: Ventral NOS",
"C02.3 Tongue: Anterior NOS",
"C02.4 Lingual Tonsil",
"C02.8 Tongue: Overlapping",
"C02.9 Tongue: NOS",
"C03.0 Gum: Upper",
"C03.1 Gum: Lower",
"C03.9 Gum NOS",
"C04.0 Mouth: Anterior Floor",
"C04.1 Mouth: Lateral Floor",
"C04.8 Mouth: Overlapping Floor",
"C04.9 Floor of Mouth NOS",
"C05.0 Hard Palate",
"C05.1 Soft Palate NOS",
"C05.2 Uvula",
"C05.8 Palate: Overlapping",
"C05.9 Palate NOS",
"C06.0 Cheek Mucosa",
"C06.1 Mouth: Vestibule",
"C06.2 Retromolar Area",
"C06.8 Mouth: Other Overlapping",
"C06.9 Mouth NOS",
"C07.9 Parotid Gland",
  "C09.8 Tonsil: Overlapping",
  "C09.9 Tonsil NOS",
  "C11.1 Nasopharynx: Poster Wall", 
  "C14.2 Waldeyer Ring",
  "C30.0 Nasal Cavity",
  "C37.9 Thymus",
"C42.0 Blood",
  "C42.2 Spleen",
"C42.4 Hematopoietic NOS",

 #skin
"C44.0 Skin of lip, NOS",
"C44.1 Eyelid",
"C44.2 External ear",
"C44.3 Skin of ear and unspecified parts of face",
"C44.4 Skin of scalp and neck",
"C44.5 Skin of trunk",
"C44.6 Skin of upper limb and shoulder",
"C44.7 Skin of lower limb and hip",
"C44.8 Overlapping lesion of skin",
"C44.9 Skin, NOS", 

#breast
"C50.0 Nipple",

#vulva/vagina
"C51.0 Labium majus",
"C51.1 Labium minus",
"C51.2 Clitoris",
"C51.8 Overlapping lesion of vulva",
"C51.9 Vulva, NOS",
"C52.9 Vagina, NOS",

#penis
"C60.0 Prepuce",
"C60.1 Glans penis",
"C60.2 Body of penis",
"C60.8 Overlapping lesion of penis",
"C60.9 Penis",
"C63.2 Scrotum, NOS",

  "C77.0 Lymph Nodes: HeadFaceNeck",
  "C77.1 Intrathoracic Lymph Nodes",
  "C77.2 Intra-abdominal LymphNodes",
  "C77.3 Lymph Nodes of axilla or arm ",
  "C77.4 Lymph Nodes: Leg",
  "C77.5 Pelvic Lymph Nodes",
  "C77.8 Lymph Nodes: multiple region",
  "C77.9 Lymph Node NOS"))


dat <- merge(dat, prim_site_text, by = "PRIMARY_SITE", all.x = TRUE) 

rm(prim_site_text)

# convert numeric variables from character class to numeric class
num_vars <- c("AGE", "CROWFLY", "TUMOR_SIZE", "DX_STAGING_PROC_DAYS", "DX_RX_STARTED_DAYS", "DX_SURG_STARTED_DAYS",
              "DX_DEFSURG_STARTED_DAYS", "SURG_DISCHARGE_DAYS", "DX_RAD_STARTED_DAYS",  "RAD_REGIONAL_DOSE_CGY",
              "RAD_BOOST_DOSE_CGY", "RAD_ELAPSED_RX_DAYS", "DX_SYSTEMIC_STARTED_DAYS", "DX_CHEMO_STARTED_DAYS", 
              "DX_HORMONE_STARTED_DAYS", "DX_OTHER_STARTED_DAYS", "DX_LASTCONTACT_DEATH_MONTHS",
              "RAD_NUM_TREAT_VOL")

dat[num_vars] <- lapply(dat[num_vars], as.numeric)


# convert factor variables from character class to factor class
vars <- names(dat)
fact_vars <- vars[!(vars %in% num_vars)] # basically all of the non-numerics

dat[fact_vars] <- lapply(dat[fact_vars], as.character)
dat[fact_vars] <- lapply(dat[fact_vars], as.factor)

dat <- dat %>%
        mutate(FACILITY_TYPE_F = fct_recode(FACILITY_TYPE_CD,
                                            "Community Cancer Program" = "1",
                                            "Comprehensive Comm Ca Program" = "2",
                                            "Academic/Research Program" = "3",
                                            "Integrated Network Ca Program" = "4",
                                            "Other" = "9")) %>%
        mutate(FACILITY_LOCATION_F = fct_recode(FACILITY_LOCATION_CD,
                                            "New England" = "1",
                                            "Middle Atlantic" = "2",
                                            "South Atlantic" = "3",
                                            "East North Central" = "4",
                                            "East South Central" = "5",
                                            "West North Central" = "6",
                                            "West South Central" = "7",
                                            "Mountain" = "8",
                                            "Pacific" = "9",
                                            "out of US" = "0")) %>%
        mutate(FACILITY_GEOGRAPHY = fct_collapse(FACILITY_LOCATION_CD,
                                                 "Northeast" = c("1", "2"),
                                                 "South" = c("3", "7"),
                                                 "Midwest" = c("4", "5", "6"),
                                                 "West" = c("8", "9"))) %>%
        mutate(AGE_F = cut(AGE, c(0, 54, 64, 74, 100))) %>%
        mutate(AGE_40 = cut(AGE, c(0, 40, 100))) %>%
        mutate(SEX_F = fct_recode(SEX,
                                "Male" = "1",
                                "Female" = "2")) %>%
        mutate(RACE_F = fct_collapse(RACE,
                                "White" = c("01"),
                                "Black" = c("02"),
                                "Asian" = c("04", "05", "06", "07", "08", "10", "11", "12", "13", "14", "15",
                                            "16", "17", "20", "21", "22", "25", "26", "27", "28", "30", "31",
                                            "32", "96", "97"),
                                "Other/Unk" = c("03", "98", "99"))) %>%
        mutate(HISPANIC = fct_collapse(SPANISH_HISPANIC_ORIGIN,
                                       "Yes" = c("1", "2", "3", "4", "5", "6", "7", "8"),
                                       "No" = c("0"),
                                       "Unknown" = c("9"))) %>%
        mutate(INSURANCE_F = fct_recode(INSURANCE_STATUS,
                                         "None" = "0",
                                         "Private" = "1",
                                         "Medicaid" = "2",
                                         "Medicare" = "3",
                                         "Other Government" = "4",
                                         "Unknown" = "9")) %>%
        mutate(INSURANCE_F = fct_relevel(INSURANCE_F,
                                         "Private")) %>%
        mutate(INCOME_F = fct_recode(MED_INC_QUAR_12,
                                      "Less than $38,000" = "1",
                                      "$38,000 - $47,999" = "2",
                                      "$48,000 - $62,999" = "3",
                                      "$63,000 +" = "4")) %>%
        mutate(EDUCATION_F = fct_recode(NO_HSD_QUAR_12,
                                        "21% or more" = "1",
                                        "13 - 20.9%" = "2",
                                        "7 - 12.9%" = "3",
                                        "Less than 7%" = "4")) %>%
        mutate(U_R_F = fct_collapse(UR_CD_13,
                                    "Metro" = c("1", "2", "3"),
                                    "Urban" = c("4", "5", "6", "7"),
                                    "Rural" = c("8", "9"))) %>%
        mutate(CLASS_OF_CASE_F = fct_collapse(CLASS_OF_CASE,
                                              All_Part_Prim = c("10", "11", "12", "13",
                                                                "14", "20", "21", "22"),
                                              Other_Facility = c("00"))) %>%
        mutate(GRADE_F = fct_recode(GRADE,
                                  "Gr I: Well Diff" = "1",
                                  "Gr II: Mod Diff" = "2",
                                  "Gr III: Poor Diff" = "3",
                                  "Gr IV: Undiff/Anaplastic" = "4",
                                  "NA/Unkown" = "9")) %>%
        mutate(HISTOLOGY_F = fct_infreq(HISTOLOGY)) %>%
        mutate(HISTOLOGY_F = factor(HISTOLOGY_F)) %>%
        mutate(HISTOLOGY_F_LIM = fct_lump(HISTOLOGY_F, prop = 0.05)) %>%
        mutate(TNM_CLIN_T = fct_recode(TNM_CLIN_T,
                                       "N_A" = "88")) %>%
        mutate(TNM_CLIN_T = fct_relevel(TNM_CLIN_T,
                                        "1")) %>%
        mutate(TNM_CLIN_N = fct_recode(TNM_CLIN_N,
                                       "N_A" = "88")) %>%
        mutate(TNM_CLIN_M = fct_recode(TNM_CLIN_M,
                                       "N_A" = "88")) %>%
        mutate(TNM_PATH_T = fct_recode(TNM_PATH_T,
                                       "N_A" = "88")) %>%
        mutate(TNM_PATH_T = fct_relevel(TNM_PATH_T,
                                        "1")) %>%
        mutate(TNM_PATH_N = fct_recode(TNM_PATH_N,
                                       "N_A" = "88")) %>%
        mutate(TNM_PATH_M = fct_recode(TNM_PATH_M,
                                       "N_A" = "88")) %>%
        mutate(TNM_CLIN_STAGE_GROUP = fct_recode(TNM_CLIN_STAGE_GROUP,
                                       "N_A" = "88")) %>%
        mutate(TNM_PATH_STAGE_GROUP = fct_recode(TNM_PATH_STAGE_GROUP,
                                       "N_A" = "88")) %>%
        mutate(MARGINS = fct_recode(RX_SUMM_SURGICAL_MARGINS,
                                    "No Residual" = "0",
                                    "Residual, NOS" = "1",
                                    "Microscopic Resid" = "2",
                                    "Macroscopic Resid" = "3",
                                    "Not evaluable" = "7",
                                    "No surg" = "8",
                                    "Unknown" = "9")) %>%
        mutate(MARGINS_YN = fct_collapse(RX_SUMM_SURGICAL_MARGINS,
                                         "Yes" = c("1", "2", "3"),
                                         "No" = c("0"),
                                         "No surg/Unk/NA" = c("7", "8", "9"))) %>%
        mutate(READM_HOSP_30_DAYS_F = fct_recode(READM_HOSP_30_DAYS,
                                                 "No_Surg_or_No_Readmit" = "0",
                                                 "Unplan_Readmit_Same" = "1",
                                                 "Plan_Readmit_Same" = "2",
                                                 "PlanUnplan_Same" = "3",
                                                 "Unknown" = "4")) %>%
        mutate(RX_SUMM_RADIATION_F = fct_recode(RX_SUMM_RADIATION,
                                                "None" = "0",
                                                "Beam Radiation" = "1",
                                                "Radioactive Implants" = "2",
                                                "Radioisotopes" = "3",
                                                "Beam + Imp or Isotopes" = "4",
                                                "Radiation, NOS" = "5",
                                                "Unknown" = "9")) %>%
        mutate(PUF_30_DAY_MORT_CD_F = fct_recode(PUF_30_DAY_MORT_CD,
                                                 "Alive_30" = "0",
                                                 "Dead_30" = "1",
                                                 "Unknown" = "9")) %>%
        mutate(PUF_90_DAY_MORT_CD_F = fct_recode(PUF_90_DAY_MORT_CD,
                                                 "Alive_90" = "0",
                                                 "Dead_90" = "1",
                                                 "Unknown" = "9")) %>%
        mutate(LYMPH_VASCULAR_INVASION_F = fct_recode(LYMPH_VASCULAR_INVASION,
                                                      "Neg_LymphVasc_Inv" = "0",
                                                      "Pos_LumphVasc_Inv" = "1",
                                                      "N_A" = "8",
                                                      "Unknown" = "9")) %>%
        mutate(RX_HOSP_SURG_APPR_2010_F = fct_recode(RX_HOSP_SURG_APPR_2010,
                                                     "No_Surg" = "0",
                                                     "Robot_Assist" = "1",
                                                     "Robot_to_Open" = "2",
                                                     "Endo_Lap" = "3",
                                                     "Endo_Lap_to_Open" = "4",
                                                     "Open_Unknown" = "5",
                                                     "Unknown" = "9")) %>%
        mutate(All = "All") %>%
        mutate(All = factor(All)) %>%
        mutate(REASON_FOR_NO_SURGERY_F = fct_recode(REASON_FOR_NO_SURGERY,
                                                    "Surg performed" = "0",
                                                    "Surg not recommended" = "1",
                                                    "No surg due to pt factors" = "2",
                                                    "No surg, pt died" = "5",
                                                    "Surg rec, not done" = "6",
                                                    "Surg rec, pt refused" = "7",
                                                    "Surg rec, unk if done" = "8",
                                                    "Unknown" = "9")) %>%
        mutate(SURGERY_YN = ifelse(REASON_FOR_NO_SURGERY == "0",
                                   "Yes",
                                   ifelse(REASON_FOR_NO_SURGERY == "9",
                                          "Ukn",
                                          "No"))) %>%
        mutate(SURG_TF = case_when(SURGERY_YN == "Yes" ~ TRUE,
                             SURGERY_YN == "No" ~ FALSE,
                             TRUE ~ NA))  %>%
        mutate(REASON_FOR_NO_RADIATION_F = fct_recode(REASON_FOR_NO_RADIATION,
                                                    "Rad performed" = "0",
                                                    "Rad not recommended" = "1",
                                                    "No Rad due to pt factors" = "2",
                                                    "No Rad, pt died" = "5",
                                                    "Rad rec, not done" = "6",
                                                    "Rad rec, pt refused" = "7",
                                                    "Rad rec, unk if done" = "8",
                                                    "Unknown" = "9")) %>%
        mutate(RADIATION_YN = ifelse(REASON_FOR_NO_RADIATION == "0",
                                   "Yes",
                                   ifelse(REASON_FOR_NO_RADIATION == "9",
                                          NA,
                                          "No"))) %>%
        mutate(SURGRAD_SEQ_F = fct_recode(RX_SUMM_SURGRAD_SEQ,
                                                   "None or Surg or Rad" = "0",
                                                   "Rad before Surg" = "2",
                                                   "Surg before Rad" = "3",
                                                   "Rad before and after Surg" = "4",
                                                   "Intraop Rad" = "5",
                                                   "Intraop Rad plus other" = "6",
                                                   "Unknown" = "9")) %>%
        mutate(SURG_RAD_SEQ = ifelse(SURGERY_YN == "Yes" & RX_SUMM_SURGRAD_SEQ == "0",
                                     "Surg Alone",
                                     ifelse(RADIATION_YN == "Yes" & RX_SUMM_SURGRAD_SEQ == "0",
                                            "Rad Alone",
                                            ifelse(SURGERY_YN == "No" & RADIATION_YN == "No" & RX_SUMM_SURGRAD_SEQ == "0",
                                                   "No Treatment",
                                                   ifelse(RX_SUMM_SURGRAD_SEQ == "2",
                                                          "Rad then Surg",
                                                          ifelse(RX_SUMM_SURGRAD_SEQ == "3",
                                                                 "Surg then Rad",
                                                                 ifelse(RX_SUMM_SURGRAD_SEQ == "4",
                                                                        "Rad before and after Surg",
                                                                        "Other"))))))) %>%
        mutate(SURG_RAD_SEQ = fct_relevel(SURG_RAD_SEQ,
                                          "Surg Alone",
                                          "Surg then Rad",
                                          "Rad Alone")) %>%
        mutate(CHEMO_YN = fct_collapse(RX_SUMM_CHEMO,
                                       "No" = c("00", "82", "85", "86", "87"),
                                       "Yes" = c("01", "02", "03"),
                                       "Ukn" = c("88", "99"))) %>%
        mutate(IMMUNO_YN = fct_collapse(RX_SUMM_IMMUNOTHERAPY,
                                       "No" = c("00", "82", "85", "86", "87"),
                                       "Yes" = c("01"),
                                       "Ukn" = c("88", "99"))) %>%
        mutate(SURG_RAD_SEQ_C = ifelse(SURGERY_YN == "Yes" & RX_SUMM_SURGRAD_SEQ == "0" & CHEMO_YN == "No",
                                     "Surg, No rad, No Chemo",
                                     ifelse(RADIATION_YN == "Yes" & RX_SUMM_SURGRAD_SEQ == "0" & CHEMO_YN == "No",
                                            "Rad, No Surg, No Chemo",
                                            ifelse(SURGERY_YN == "No" & RADIATION_YN == "No" & RX_SUMM_SURGRAD_SEQ == "0" & CHEMO_YN == "No",
                                                   "No Surg, No Rad, No Chemo",
                                                   ifelse(RX_SUMM_SURGRAD_SEQ == "2" & CHEMO_YN == "No",
                                                          "Rad then Surg, No Chemo",
                                                          ifelse(RX_SUMM_SURGRAD_SEQ == "3" & CHEMO_YN == "No",
                                                                 "Surg then Rad, No Chemo",
                                                                 ifelse(RX_SUMM_SURGRAD_SEQ == "4" & CHEMO_YN == "No",
                                                                        "Rad before and after Surg, No Chemo",
                                ifelse(SURGERY_YN == "Yes" & RX_SUMM_SURGRAD_SEQ == "0" & CHEMO_YN == "Yes",
                                       "Surg, No rad, Yes Chemo",
                                       ifelse(RADIATION_YN == "Yes" & RX_SUMM_SURGRAD_SEQ == "0" & CHEMO_YN == "Yes",
                                              "Rad, No Surg, Yes Chemo",
                                              ifelse(SURGERY_YN == "No" & RADIATION_YN == "No" & RX_SUMM_SURGRAD_SEQ == "0" & CHEMO_YN == "Yes",
                                                     "No Surg, No Rad, Yes Chemo",
                                                     ifelse(RX_SUMM_SURGRAD_SEQ == "2" & CHEMO_YN == "Yes",
                                                            "Rad then Surg, Yes Chemo",
                                                            ifelse(RX_SUMM_SURGRAD_SEQ == "3" & CHEMO_YN == "Yes",
                                                                   "Surg then Rad, Yes Chemo",
                                                                   ifelse(RX_SUMM_SURGRAD_SEQ == "4" & CHEMO_YN == "Yes",
                                                                          "Rad before and after Surg, Yes Chemo",
                                                                          "Other"))))))))))))) %>%
        mutate(SURG_RAD_SEQ_C = fct_infreq(SURG_RAD_SEQ_C)) %>%
        mutate(T_SIZE = as.numeric(TUMOR_SIZE)) %>%
        mutate(T_SIZE = ifelse(T_SIZE == 0,
                                "No Tumor",
                                ifelse(T_SIZE > 0 & T_SIZE < 10 | T_SIZE == 991,
                                       "< 1 cm",
                                       ifelse(T_SIZE >= 10 & T_SIZE < 20 | T_SIZE == 992,
                                              "1-2 cm",
                                              ifelse(T_SIZE >= 20 & T_SIZE < 30 | T_SIZE == 993,
                                                     "2-3 cm",
                                                     ifelse(T_SIZE >= 30 & T_SIZE < 40 | T_SIZE == 994,
                                                            "3-4 cm",
                                                            ifelse(T_SIZE >= 40 & T_SIZE < 50 | T_SIZE == 995,
                                                                   "4-5 cm",
                                                                   ifelse(T_SIZE >= 50 & T_SIZE < 60 | T_SIZE == 996,
                                                                          "5-6 cm",
                                                                          ifelse(T_SIZE >= 60 & T_SIZE <= 987 |
                                                                                         T_SIZE == 980 | T_SIZE == 989 |
                                                                                         T_SIZE == 997,
                                                                          ">6 cm",
                                                                          ifelse(T_SIZE == 988 | T_SIZE == 999,
                                                                                 "NA_unk",
                                                                                 "Microscopic focus")))))))))) %>%
        mutate(T_SIZE = factor(T_SIZE)) %>%
        mutate(T_SIZE = fct_relevel(T_SIZE,
                                     "No Tumor", "Microscopic focus", "< 1 cm", "1-2 cm", "2-3 cm", "3-4 cm",
                                       "4-5 cm", "5-6 cm", ">6 cm", "NA_unk")) %>%
        mutate(mets_at_dx = case_when(CS_METS_DX_LUNG == "1" ~ "Lung",
                                      CS_METS_DX_BONE == "1" ~ "Bone",
                                      CS_METS_DX_BRAIN == "1" ~ "Brain",
                                      CS_METS_DX_LIVER == "1" ~ "Liver",
                                      TRUE ~ "None/Other/Unk/NA")) %>%
        mutate(MEDICAID_EXPN_CODE = fct_recode(MEDICAID_EXPN_CODE,
                                               "Non-Expansion State" = "0",
                                               "Jan 2014 Expansion States" = "1",
                                               "Early Expansion States (2010-13)" = "2",
                                               "Late Expansion States (> Jan 2014)" = "3",
                                               "Suppressed for Ages 0 - 39" = "9"))  %>%
        mutate(EXPN_GROUP =  case_when(MEDICAID_EXPN_CODE  %in% c("Jan 2014 Expansion States") & 
                                         YEAR_OF_DIAGNOSIS %in% c("2014", "2015") ~ "Post-Expansion",
                                       
                                       MEDICAID_EXPN_CODE  %in% c("Jan 2014 Expansion States") & 
                                         YEAR_OF_DIAGNOSIS %in% 
                                          c("2004", "2005", "2006", "2007", "2008", 
                                            "2009", "2010", "2011", "2012", "2013") ~ "Pre-Expansion",
               
                                       MEDICAID_EXPN_CODE  %in% c("Early Expansion States (2010-13)") & 
                                         YEAR_OF_DIAGNOSIS %in% c("2010", "2011", "2012", "2013", "2014", "2015") ~ "Post-Expansion",
                                       
                                        MEDICAID_EXPN_CODE  %in% c("Early Expansion States (2010-13)") & 
                                         YEAR_OF_DIAGNOSIS %in% c("2004", "2005", "2006", "2007", "2008", "2009") ~ "Pre-Expansion",

                                       MEDICAID_EXPN_CODE %in% c("Non-Expansion State") ~ "Pre-Expansion",

                                       MEDICAID_EXPN_CODE %in% c("Late Expansion States (> Jan 2014)") ~ "Pre-Expansion",
                    
                                       MEDICAID_EXPN_CODE %in% c("Late Expansion States (> Jan 2014)") & 
                                        YEAR_OF_DIAGNOSIS %in% c("2014", "2015") ~ "Exclude",
                                       
                                       MEDICAID_EXPN_CODE == "Suppressed for Ages 0 - 39" ~ "Exclude")) %>%
  
  mutate(pre_2014 = YEAR_OF_DIAGNOSIS %in% c("2004", "2005", "2006", "2007", "2008", 
                                            "2009", "2010", "2011", "2012", "2013")) %>%
  
  mutate(mets_at_dx_F = ifelse(mets_at_dx == "None/Other/Unk/NA", FALSE, TRUE)) %>% 
  
  mutate(Tx_YN = ifelse(SURG_RAD_SEQ == "No Treatment" & CHEMO_YN == "No" & 
                          IMMUNO_YN == "No", FALSE, 
                        ifelse(CHEMO_YN == "Ukn", NA, 
                               TRUE)))

fact_vars_2 <- c("FACILITY_TYPE_F", "FACILITY_LOCATION_F", "AGE_F", "SEX_F", "RACE_F",
                 "HISPANIC", "INSURANCE_F", "INCOME_F", "EDUCATION_F", "U_R_F",
                 "CDCC_TOTAL_BEST", "CLASS_OF_CASE_F", "YEAR_OF_DIAGNOSIS", "PRIMARY_SITE", "HISTOLOGY",
                 "BEHAVIOR", "GRADE_F", "TNM_CLIN_T", "TNM_CLIN_N", "TNM_CLIN_M",
                 "TNM_CLIN_STAGE_GROUP", "TNM_PATH_T", "TNM_PATH_N", "TNM_PATH_M", "TNM_PATH_STAGE_GROUP",
                 "MARGINS", "READM_HOSP_30_DAYS_F", "RX_SUMM_RADIATION_F", "PUF_30_DAY_MORT_CD_F",
                 "PUF_90_DAY_MORT_CD_F", "LYMPH_VASCULAR_INVASION_F", "RX_HOSP_SURG_APPR_2010_F", "mets_at_dx")


dat <- dat %>%
        mutate_at(fact_vars_2, funs(factor(.)))

```


# Extract data of interest 

```{r}
# EMPD
site_code <- c(
  #lip  
  "C000", "C001", "C002", "C003", "C004", "C005","C006", "C008","C009",
                                  
                                 
#skin/melanoma
  "C440", "C441", "C442", "C443", "C444", "C445", "C446", "C447", "C448", "C449",
                                 
                                 
#vagina/vulva
  "C510", "C511", "C512", "C518", "C519", "C529",
                                 
#penis
 "C600", "C601", "C602", "C608", "C609", "C639")

histo_code <- c("8542")
behavior_code <- c("3")

data <- dat %>%
        filter(BEHAVIOR %in% behavior_code) %>%
        filter(PRIMARY_SITE %in% site_code) %>%
        filter(HISTOLOGY %in% histo_code) %>%
        filter(is.na(PUF_VITAL_STATUS) == FALSE) %>%
        filter(is.na(DX_LASTCONTACT_DEATH_MONTHS) == FALSE) %>%
        filter(SEQUENCE_NUMBER == "00") 

no_Excludes <- as.data.frame(data %>% 
                               filter(EXPN_GROUP != "Exclude") 
                             %>% droplevels())
```

```{r saveloadData}
#file_path <- c("/Users/beastatlife/Google Drive/Penn/Research/Barbieri/NCDB")
#save(data,
#      file = paste0(file_path, "/EMPD_data.Rda"))

#load("EMPD_data.Rda")
```




Data including skin tumors was obtained from the NCBD on October 7, 2019. Cases that were included in this analysis were those with:

1. Site codes: `r site_code`
2. Histology codes: `r histo_code`
3. Behavior codes: `r behavior_code`


Patients were excluded if they didn't have values for either follow up or vital status.

Patients were excluded if they had surgery to a distant site using `RX_SUMM_SURG_OTH_REGDIS`. This was done to avoid confounding of different surgical procedures. We are only interested in surgery at the primary site. These distant site surgeries were being counted in the surgery/radiation sequence and thus to simplify analysis they were removed. 

```{r}

data %>%
        CreateTableOne(data = .,
                     vars = c("RX_SUMM_SURG_OTH_REGDIS"),
                     includeNA = TRUE) %>%
        print(.,
              showAllLevels = TRUE)

data <- data %>%
        filter(RX_SUMM_SURG_OTH_REGDIS == "0") 
```


Race was grouped as white, black, asian, other/unknown
Stage was grouped into 0, I, II, III, IV, NA_Unknown, stage 0 was removed
Whether surgery was performed was based on the `REASON_FOR_NO_SURGERY` variable. The `SURGERY_YN` variable was classified as 'Yes', 'No', or 'Unknown'.


Whether radiation was performed was based on the `REASON_FOR_NO_RADIATION` variable. The `RADIATION_YN` variable was classified as 'Yes', 'No', or 'Unknown'.



 ##Table of variables for all cases:

```{r}

p_table(data,
        vars = c("FACILITY_TYPE_F", "FACILITY_LOCATION_F", "FACILITY_GEOGRAPHY",  "AGE", "AGE_F", "AGE_40",
                 "SEX_F", "RACE_F", "HISPANIC", "INSURANCE_F", 
                 "INCOME_F", "EDUCATION_F", "U_R_F", "CROWFLY", "CDCC_TOTAL_BEST",
                 "SITE_TEXT",  "BEHAVIOR", "GRADE_F",
                 "DX_STAGING_PROC_DAYS", "TNM_CLIN_T", "TNM_CLIN_N", "TNM_CLIN_M",
                 "TNM_CLIN_STAGE_GROUP", "TNM_PATH_T", "TNM_PATH_N", "TNM_PATH_M",
                 "TNM_PATH_STAGE_GROUP", "DX_RX_STARTED_DAYS", "DX_SURG_STARTED_DAYS",
                 "DX_DEFSURG_STARTED_DAYS", "MARGINS", "MARGINS_YN", "SURG_DISCHARGE_DAYS",
                 "READM_HOSP_30_DAYS_F", "RX_SUMM_RADIATION_F", "PUF_30_DAY_MORT_CD_F",
                 "PUF_90_DAY_MORT_CD_F", "DX_LASTCONTACT_DEATH_MONTHS", 
                 "LYMPH_VASCULAR_INVASION_F", "RX_HOSP_SURG_APPR_2010_F", "SURG_RAD_SEQ",
                 "SURG_RAD_SEQ_C", "SURGERY_YN", "RADIATION_YN", "CHEMO_YN", 
                 "IMMUNO_YN", "Tx_YN", "mets_at_dx",
                 "MEDICAID_EXPN_CODE", "EXPN_GROUP"))

```

```{r}
p_table(data,
        vars = c("FACILITY_TYPE_F", "FACILITY_LOCATION_F", "FACILITY_GEOGRAPHY",  "AGE", "AGE_F", "AGE_40",
                 "SEX_F", "RACE_F", "HISPANIC", "INSURANCE_F", 
                 "INCOME_F", "EDUCATION_F", "U_R_F", "CROWFLY", "CDCC_TOTAL_BEST",
                 "SITE_TEXT", "BEHAVIOR", "GRADE_F",
                 "DX_STAGING_PROC_DAYS", "TNM_CLIN_T", "TNM_CLIN_N", "TNM_CLIN_M",
                 "TNM_CLIN_STAGE_GROUP", "TNM_PATH_T", "TNM_PATH_N", "TNM_PATH_M",
                 "TNM_PATH_STAGE_GROUP", "DX_RX_STARTED_DAYS", "DX_SURG_STARTED_DAYS",
                 "DX_DEFSURG_STARTED_DAYS", "MARGINS", "MARGINS_YN", "SURG_DISCHARGE_DAYS",
                 "READM_HOSP_30_DAYS_F", "RX_SUMM_RADIATION_F", "PUF_30_DAY_MORT_CD_F",
                 "PUF_90_DAY_MORT_CD_F", "DX_LASTCONTACT_DEATH_MONTHS", 
                 "LYMPH_VASCULAR_INVASION_F", "RX_HOSP_SURG_APPR_2010_F", "SURG_RAD_SEQ",
                 "SURG_RAD_SEQ_C", "T_SIZE", "SURGERY_YN", "RADIATION_YN", "CHEMO_YN",
                 "IMMUNO_YN", "Tx_YN", "mets_at_dx",
                 "MEDICAID_EXPN_CODE"), 
        strata = "SURGERY_YN")


p_table(data,
        vars = c("FACILITY_TYPE_F", "FACILITY_LOCATION_F", "FACILITY_GEOGRAPHY",  "AGE", "AGE_F", "AGE_40",
                 "SEX_F", "RACE_F", "HISPANIC", "INSURANCE_F", 
                 "INCOME_F", "EDUCATION_F", "U_R_F", "CROWFLY", "CDCC_TOTAL_BEST",
                 "SITE_TEXT", "BEHAVIOR", "GRADE_F",
                 "DX_STAGING_PROC_DAYS", "TNM_CLIN_T", "TNM_CLIN_N", "TNM_CLIN_M",
                 "TNM_CLIN_STAGE_GROUP", "TNM_PATH_T", "TNM_PATH_N", "TNM_PATH_M",
                 "TNM_PATH_STAGE_GROUP", "DX_RX_STARTED_DAYS", "DX_SURG_STARTED_DAYS",
                 "DX_DEFSURG_STARTED_DAYS", "MARGINS", "MARGINS_YN", "SURG_DISCHARGE_DAYS",
                 "READM_HOSP_30_DAYS_F", "RX_SUMM_RADIATION_F", "PUF_30_DAY_MORT_CD_F",
                 "PUF_90_DAY_MORT_CD_F", "DX_LASTCONTACT_DEATH_MONTHS", 
                 "LYMPH_VASCULAR_INVASION_F", "RX_HOSP_SURG_APPR_2010_F", "SURG_RAD_SEQ",
                 "SURG_RAD_SEQ_C", "T_SIZE", "SURGERY_YN", "RADIATION_YN", 
                 "CHEMO_YN", "IMMUNO_YN", "Tx_YN", "mets_at_dx",
                 "MEDICAID_EXPN_CODE"), 
        strata = "RADIATION_YN")

p_table(data,
        vars = c("FACILITY_TYPE_F", "FACILITY_LOCATION_F", "FACILITY_GEOGRAPHY",  "AGE", "AGE_F", "AGE_40",
                 "SEX_F", "RACE_F", "HISPANIC", "INSURANCE_F", 
                 "INCOME_F", "EDUCATION_F", "U_R_F", "CROWFLY", "CDCC_TOTAL_BEST",
                 "SITE_TEXT", "BEHAVIOR", "GRADE_F",
                 "DX_STAGING_PROC_DAYS", "TNM_CLIN_T", "TNM_CLIN_N", "TNM_CLIN_M",
                 "TNM_CLIN_STAGE_GROUP", "TNM_PATH_T", "TNM_PATH_N", "TNM_PATH_M",
                 "TNM_PATH_STAGE_GROUP", "DX_RX_STARTED_DAYS", "DX_SURG_STARTED_DAYS",
                 "DX_DEFSURG_STARTED_DAYS", "MARGINS", "MARGINS_YN", "SURG_DISCHARGE_DAYS",
                 "READM_HOSP_30_DAYS_F", "RX_SUMM_RADIATION_F", "PUF_30_DAY_MORT_CD_F",
                 "PUF_90_DAY_MORT_CD_F", "DX_LASTCONTACT_DEATH_MONTHS", 
                 "LYMPH_VASCULAR_INVASION_F", "RX_HOSP_SURG_APPR_2010_F", "SURG_RAD_SEQ",
                 "SURG_RAD_SEQ_C", "T_SIZE", "SURGERY_YN", "RADIATION_YN", 
                 "CHEMO_YN", "IMMUNO_YN", "Tx_YN","mets_at_dx",
                 "MEDICAID_EXPN_CODE"), 
        strata = "CHEMO_YN")


p_table(data,
        vars = c("FACILITY_TYPE_F", "FACILITY_LOCATION_F", "FACILITY_GEOGRAPHY",  "AGE", "AGE_F", "AGE_40",
                 "SEX_F", "RACE_F", "HISPANIC", "INSURANCE_F", 
                 "INCOME_F", "EDUCATION_F", "U_R_F", "CROWFLY", "CDCC_TOTAL_BEST",
                 "SITE_TEXT", "BEHAVIOR", "GRADE_F",
                 "DX_STAGING_PROC_DAYS", "TNM_CLIN_T", "TNM_CLIN_N", "TNM_CLIN_M",
                 "TNM_CLIN_STAGE_GROUP", "TNM_PATH_T", "TNM_PATH_N", "TNM_PATH_M",
                 "TNM_PATH_STAGE_GROUP", "DX_RX_STARTED_DAYS", "DX_SURG_STARTED_DAYS",
                 "DX_DEFSURG_STARTED_DAYS", "MARGINS", "MARGINS_YN", "SURG_DISCHARGE_DAYS",
                 "READM_HOSP_30_DAYS_F", "RX_SUMM_RADIATION_F", "PUF_30_DAY_MORT_CD_F",
                 "PUF_90_DAY_MORT_CD_F", "DX_LASTCONTACT_DEATH_MONTHS", 
                 "LYMPH_VASCULAR_INVASION_F", "RX_HOSP_SURG_APPR_2010_F", "SURG_RAD_SEQ",
                 "SURG_RAD_SEQ_C", "T_SIZE", "SURGERY_YN", "RADIATION_YN", 
                 "CHEMO_YN", "mets_at_dx", "IMMUNO_YN", "Tx_YN",
                 "MEDICAID_EXPN_CODE"), 
        strata = "Tx_YN")
```







#Kaplan Meier Analysis


##All

```{r}
uni_var(test_var = "All", data_imp = data)
```

##Facility Type
```{r}
uni_var(test_var = "FACILITY_TYPE_F", data_imp = data)
```

##Facility Location

```{r}
uni_var(test_var = "FACILITY_LOCATION_F", data_imp = data)
```

##Facility Geography

```{r}
uni_var(test_var = "FACILITY_GEOGRAPHY", data_imp = data)
```

##Age Group

```{r}
uni_var(test_var = "AGE_F", data_imp = data)
```

##Age Group
```{r}
uni_var(test_var = "AGE_40", data_imp = data)
```

##Gender

```{r}
uni_var(test_var = "SEX_F", data_imp = data)
```

##RACE_F

```{r}
uni_var(test_var = "RACE_F", data_imp = data)
```

##Hispanic

```{r}
uni_var(test_var = "HISPANIC", data_imp = data)
```

##Insurance Status

```{r}
uni_var(test_var = "INSURANCE_F", data_imp = data)
```

##Overall Survival pre/post-ACA expansion

```{r}
uni_var(test_var = "EXPN_GROUP", data_imp = no_Excludes)
```


<!-- ##Income -->

<!-- ```{r} -->
<!-- class(data$INCOME_F) -->
<!-- uni_var(test_var = "INCOME_F", data_imp = data) -->
<!-- ``` -->

##Education

```{r}
uni_var(test_var = "EDUCATION_F", data_imp = data)
```

##Urban/Rural

```{r}
uni_var(test_var = "U_R_F", data_imp = data)
```

##Class (treatment at performing facility)

```{r}
uni_var(test_var = "CLASS_OF_CASE_F", data_imp = data)
```

##Year

```{r}
uni_var(test_var = "YEAR_OF_DIAGNOSIS", data_imp = data)
```

##Primary Site

```{r}
uni_var(test_var = "SITE_TEXT", data_imp = data)
```


##Histology

```{r}
#uni_var(test_var = "HISTOLOGY_F_LIM", data_imp = data)
```

<!-- ##Behavior -->

<!-- ```{r} -->
<!-- uni_var(test_var = "BEHAVIOR", data_imp = data) -->
<!-- ``` -->

##Grade

```{r}
uni_var(test_var = "GRADE_F", data_imp = data)
```

##Clinical T Stage

```{r}
#uni_var(test_var = "TNM_CLIN_T", data_imp = data)
```

##Clinical N Stage

```{r}
#uni_var(test_var = "TNM_CLIN_N", data_imp = data)
```

##Clinical M Stage

```{r}
#uni_var(test_var = "TNM_CLIN_M", data_imp = data)
```

##Clinical Stage Group

```{r}
uni_var(test_var = "TNM_CLIN_STAGE_GROUP", data_imp = data)
```

##Pathologic T Stage

```{r}
#uni_var(test_var = "TNM_PATH_T", data_imp = data)
```

##Pathologic N Stage

```{r}
#uni_var(test_var = "TNM_PATH_N", data_imp = data)
```

##Pathologic M Stage

```{r}
#uni_var(test_var = "TNM_PATH_M", data_imp = data)
```

##Pathologic Stage Group

```{r}
uni_var(test_var = "TNM_PATH_STAGE_GROUP", data_imp = data)
```

##Margins
```{r}
uni_var(test_var = "MARGINS", data_imp = data)
```

##Margins Yes/No
```{r}
#uni_var(test_var = "MARGINS_YN", data_imp = data)
```

##30 Day Readmission

```{r}
uni_var(test_var = "READM_HOSP_30_DAYS_F", data_imp = data)
```

##Radiation Type

```{r}
uni_var(test_var = "RX_SUMM_RADIATION_F", data_imp = data)
```


##Lymphovascular Invasion

```{r}
uni_var(test_var = "LYMPH_VASCULAR_INVASION_F", data_imp = data)
```

##Endoscopic/Robotic

```{r}
uni_var(test_var = "RX_HOSP_SURG_APPR_2010_F", data_imp = data)
```

##Surgery Radiation Sequence 

```{r}
uni_var(test_var = "SURG_RAD_SEQ", data_imp = data)
```

##Surgery Yes/No

```{r}
uni_var(test_var = "SURGERY_YN", data_imp = data)
```

##Radiation Yes/No

```{r}
uni_var(test_var = "RADIATION_YN", data_imp = data)
```

##Chemo Yes/No

```{r}
uni_var(test_var = "CHEMO_YN", data_imp = data)
```


##Treatment Yes/No
```{r}
uni_var(test_var = "Tx_YN", data_imp = data)
```

##Metastases at Dx
```{r}
uni_var(test_var = "mets_at_dx_F", data_imp = data)
```

<!-- ##Tumor Size -->

<!-- ```{r} -->
<!-- uni_var(test_var = "T_SIZE", data_imp = data) -->
<!-- ``` -->

#Tumor specific Variables


###Node Size


#Cox Proportional Hazard Ratio

##Model #1

###Full analysis

```{r}
model_one <- coxph(Surv(DX_LASTCONTACT_DEATH_MONTHS, PUF_VITAL_STATUS == 0)
                     ~ FACILITY_TYPE_F + FACILITY_LOCATION_F + CROWFLY + 
                 DX_STAGING_PROC_DAYS + 
                 CHEMO_YN + RADIATION_YN + SURGERY_YN + IMMUNO_YN +`
                 AGE_F + SEX_F + RACE_F + HISPANIC + INSURANCE_F + INCOME_F + 
                 EDUCATION_F + YEAR_OF_DIAGNOSIS,
                     data = data)
model_one %>% summary()


```

###Summary of Model

```{r}
model_one %>%
        tidy(., exponentiate = TRUE) %>%
        select(term, estimate, conf.low, conf.high, p.value) %>%
        rename(Variable = term,
               Hazard_Ratio = estimate) %>%
        tbl_df %>%
        print(n = nrow(.))

```

## Linear Regression 
```{r}

#only include rows with known treatment information, n = 82
data2 <- data %>% filter(SURGERY_YN != "Ukn" & RADIATION_YN != "Ukn"
                         & CHEMO_YN != "Ukn" & IMMUNO_YN != "Ukn")

# include only variables with data available for at least 75% cases 
# from the following set of variables of interest:

## FACILITY_TYPE_F + FACILITY_GEOGRAPHY + CROWFLY + 
##                 DX_STAGING_PROC_DAYS + 
##                 CHEMO_YN + RADIATION_YN + SURGERY_YN + IMMUNO_YN +
##                 AGE + SEX_F + RACE_F + HISPANIC + INSURANCE_F + INCOME_F + 
##                 EDUCATION_F + YEAR_OF_DIAGNOSIS + SITE_TEXT + GRADE_F

length(which(is.na(data2$GRADE_F))) / nrow(data2)

# excluded the following in this analysis due to missing data: 
#  DX_STAGING_PROC_DAYS, GRADE_F (mostly unknowns)

fit_surv <- lm(DX_LASTCONTACT_DEATH_MONTHS ~
                 FACILITY_TYPE_F + FACILITY_LOCATION_F + CROWFLY + 
                 CHEMO_YN + RADIATION_YN + SURGERY_YN + 
                 AGE_F + RACE_F + 
                 EDUCATION_F + YEAR_OF_DIAGNOSIS + SITE_TEXT,
   data = data2)

summary(fit_surv) # R^2 = 0.3936, p < 2.2e-16

# the following variables were excluded to 
# improve the R-squared of the regression (initially R^2 = 0.3931):
# INCOME_F + INSURANCE_F + HISPANIC +  SEX_F + IMMUNO_YN +
```

# Prediction Logistic Regression Models

## Surgery
```{r}

no_Ukns <- data2 %>%
  droplevels() %>% 
  mutate(SURGERY_YN = as.logical(SURGERY_YN))

# excluded the following in this analysis due to missing data: 
#  DX_STAGING_PROC_DAYS, GRADE_F (mostly unknowns)

fit_surg <- glm(SURG_TF ~ 
                 FACILITY_TYPE_F + FACILITY_LOCATION_F + 
                 CHEMO_YN + RADIATION_YN + IMMUNO_YN +
                 AGE_F + SEX_F + RACE_F + HISPANIC + INSURANCE_F + INCOME_F + 
                 EDUCATION_F + YEAR_OF_DIAGNOSIS + SITE_TEXT,
   data = no_Ukns)

summary(fit_surg)


# the following variables were excluded to 
# improve the R-squared of the regression (initially residual = 82.534):
# none

exp(cbind("Odds ratio" = coef(fit_surg), confint.default(fit_surg, level = 0.95)))
```

## Metastasis at Time of Diagnosis
```{r}

fit_mets <- glm(mets_at_dx_F ~ 
                 FACILITY_TYPE_F + FACILITY_GEOGRAPHY + CROWFLY + 
                 AGE_F + SEX_F + RACE_F + HISPANIC + INSURANCE_F + INCOME_F + 
                 EDUCATION_F + YEAR_OF_DIAGNOSIS + SITE_TEXT,
   data = data)

# the following variables were excluded to 
# improve the R-squared of the regression (initially residual = 4.7169):
# 

summary(fit_mets)

exp(cbind("Odds ratio" = coef(fit_mets), confint.default(fit_surg, level = 0.95)))
```

